WEBVTT - The Largest Data Breaches in US History: Part I

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<v Speaker 1>Welcome to tech Stuff, a production from iHeartRadio. Hey there,

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<v Speaker 1>and welcome to tech Stuff. I'm your host, Jonathan Strickland.

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<v Speaker 1>I'm an executive producer with iHeart Podcasts and how the

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<v Speaker 1>tech are you? So recently I talked about how the

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<v Speaker 1>US Department of Justice has filed a civil antitrust lawsuit

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<v Speaker 1>against the company Live Nation Entertainment, which, among many other things,

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<v Speaker 1>operates the service Ticketmaster, a service that I would say

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<v Speaker 1>has fostered a lot of very strong opinions among concertgoers,

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<v Speaker 1>including yours. Truly, I have very strong feelings about Ticketmaster.

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<v Speaker 1>But last Friday night, which was the night of May

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<v Speaker 1>thirty first, two thy twenty four for those of y'all

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<v Speaker 1>listening from the future, Ticketmaster was in the news for

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<v Speaker 1>a reason because the company had been the target of

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<v Speaker 1>hackers who allegedly stole data belonging to around five hundred

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<v Speaker 1>sixty million ticket Master customers. Now, that data reportedly includes

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<v Speaker 1>personal information like names, addresses, and phone numbers, as well

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<v Speaker 1>as purchase history. So you know, that means the hackers

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<v Speaker 1>can check and see if a you know, very public

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<v Speaker 1>punk rocker type has secretly been sneaking off to watch

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<v Speaker 1>Taylor Swift concerts or something, and also some partial credit

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<v Speaker 1>card information like the last four digits on credit cards.

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<v Speaker 1>Ticketmaster slash Live Nation initially kept quiet about this revelation,

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<v Speaker 1>but then late on Friday confirmed that a data breach

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<v Speaker 1>did in fact happen. This is a problem for lots

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<v Speaker 1>of reasons. I mean, anytime there's a data breach, that's

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<v Speaker 1>a problem, But when you're talking about a data breach

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<v Speaker 1>affecting hundreds of millions of people, that just spells a

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<v Speaker 1>massive headache moving forward. And we'll talk a lot about

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<v Speaker 1>why that is in this episode, But really I thought

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<v Speaker 1>I would chat about some of the largest data breaches

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<v Speaker 1>in US history, which is a super happy topic, right,

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<v Speaker 1>but I thought it was really important to consider how

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<v Speaker 1>technology that's meant to make systems more efficient and effective

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<v Speaker 1>can also sometimes provide an opportunity for malicious agents, for

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<v Speaker 1>hackers to make off with potentially huge amounts of information.

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<v Speaker 1>And as we all know, information is valuable. I mean,

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<v Speaker 1>it is the currency of the Internet in many ways,

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<v Speaker 1>and data breaches are becoming more and more common. The

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<v Speaker 1>Identity Theft Resource Center reported that in twenty twenty one,

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<v Speaker 1>there were one eight hundred and sixty two data breaches

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<v Speaker 1>that it was able to identify. In twenty twenty three,

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<v Speaker 1>that number was up to three thousand, two hundred five,

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<v Speaker 1>almost double. However, I feel I should clarify that twenty

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<v Speaker 1>five of those incidents were data exposures, and two of

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<v Speaker 1>them were data leaks, and fifty six were incidents that

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<v Speaker 1>weren't categorized at all. They're uncategorized, I don't know the

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<v Speaker 1>nature of them, so that leaves us three twenty two

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<v Speaker 1>cases of actual data breaches, and the differences between these

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<v Speaker 1>different categories are sometimes subtle and a little gray. As

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<v Speaker 1>for my source for what constitutes the largest data breaches

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<v Speaker 1>in the United States, I decided settle on one source

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<v Speaker 1>just for the list. Right, I went into lots of

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<v Speaker 1>sources for the details of all these things, but I

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<v Speaker 1>used a blog post on upguard dot com. It was

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<v Speaker 1>written by Kyle Chen. Now, Kyle Chen lists twenty six

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<v Speaker 1>cases of data breaches, and the Ticketmaster case isn't among them.

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<v Speaker 1>It hasn't been updated since the Ticketmaster issue. Arguably, Ticketmasters

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<v Speaker 1>should be in any list about large data breaches in

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<v Speaker 1>the United States because this was a big one. I

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<v Speaker 1>imagine when the dust settles, it could end up on

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<v Speaker 1>that list where I can't say Chen's definition. The biggest

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<v Speaker 1>isn't just in how many records were part of a

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<v Speaker 1>data breach, Like that's not the only factor that constitutes

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<v Speaker 1>whether or not it merits consideration. Also the nature of

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<v Speaker 1>the information and the impact the breach had end up

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<v Speaker 1>factoring how it falls on the list. And twenty six

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<v Speaker 1>cases is way too many cases for a podcast episode

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<v Speaker 1>or even you know, two of them. So I'm just

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<v Speaker 1>gonna go with the top ten, and even that's gonna

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<v Speaker 1>require me to break this into two episodes, and I'm

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<v Speaker 1>gonna work backward to add to the drama. By the way,

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<v Speaker 1>Kevin Chin and no point says that this is a

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<v Speaker 1>ranked list, so you could argue, I'm just giving you

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<v Speaker 1>ten random large data breach stories out of a list

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<v Speaker 1>of twenty six, and that's a legitimate criticism. But a

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<v Speaker 1>guy's got to start somewhere, right Anyway. I'm doing this

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<v Speaker 1>as a list because I've watched a lot of Jenny

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<v Speaker 1>Nicholson's older YouTube videos recently, and I absolutely love how

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<v Speaker 1>she turns everything into quote an internet friendly numbered li list.

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<v Speaker 1>In the quote, I think that's very funny. I mean,

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<v Speaker 1>Red Letter Media did the same thing with the Planket

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<v Speaker 1>reviews with all the different parts, although that was somewhat

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<v Speaker 1>necessitated by the fact that in the early days when

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<v Speaker 1>they were posting those super long reviews, YouTube videos were

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<v Speaker 1>limited to ten minutes each, so they would upload like

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<v Speaker 1>a nine part series to take down the Star Wars

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<v Speaker 1>episode one critique or whatever. Anyway, I've decided to go

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<v Speaker 1>backward in order to increase the drama. So we're gonna

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<v Speaker 1>start with number ten, which is FriendFinder Networks. And this

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<v Speaker 1>one's a doozy. So friend Fighter Networks deals with products

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<v Speaker 1>and services that include some that are not suitable for

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<v Speaker 1>a family friendly podcast. I will use some euphemisms, but

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<v Speaker 1>they include stuff like adult entertainment, webcam sites, that kind

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<v Speaker 1>of thing. That's part of what friend Fighter Networks operates.

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<v Speaker 1>The adult magazine company Penthouse bought friend Fire in early

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<v Speaker 1>twenty sixteen, and interestingly, the company operates several dating services,

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<v Speaker 1>including one intended to help people find someone with whom

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<v Speaker 1>to have casual sexual encounters, that being adult friend Finder.

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<v Speaker 1>On one end of the spectrum, and on the other

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<v Speaker 1>end of the spectrum, they have a dating service for

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<v Speaker 1>devout Christians. So I guess it's a company that really

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<v Speaker 1>does believe an equal opportunity to make money off of

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<v Speaker 1>various audiences. Anyway, as a company with businesses that are

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<v Speaker 1>in the adult entertainment sphere and social networks and also

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<v Speaker 1>dating services, FriendFinder Networks has access to a lot of

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<v Speaker 1>sensitive user information that includes info that customers absolutely would

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<v Speaker 1>prefer remain private or at least under their own control.

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<v Speaker 1>So it was a bit of a shock in late

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<v Speaker 1>twenty sixteen when news broke that hackers stole data from

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<v Speaker 1>the company that stretched back two decades, like there was

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<v Speaker 1>information in there that was twenty years old, and it

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<v Speaker 1>even included information belonging to people who had long since

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<v Speaker 1>deleted their accounts with FriendFinder Networks, but their information remained

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<v Speaker 1>on company servers despite the fact that they had deleted

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<v Speaker 1>their accounts. That seems like a very bad data ownership policy.

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<v Speaker 1>Right to retain information about people who had subsequently deleted

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<v Speaker 1>their account with you, that's a real problem. So the

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<v Speaker 1>method that these hackers used relied on LFI, which is

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<v Speaker 1>local file intrusion or sometimes local file insertion. It kind

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<v Speaker 1>of depends upon who you're talking to, but the name

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<v Speaker 1>sort of explains how this works. The hacker injects essentially

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<v Speaker 1>malicious directions into a system, and they do this usually

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<v Speaker 1>by incorporating those directions into a file, so, for example,

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<v Speaker 1>a multimedia file. This multimedia file might contain basic directory

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<v Speaker 1>commands within the file itself, so essentially it tells the system,

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<v Speaker 1>hey execute these commands in this order, and if the

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<v Speaker 1>server isn't protected against such relatively simple attacks, if I'm

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<v Speaker 1>being honest, then the code can prompt the web server

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<v Speaker 1>to configure the file improperly and give backdoor access to

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<v Speaker 1>a hacker, which is in fact what happened in this case.

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<v Speaker 1>The hackers got access to information stored on the affected servers,

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<v Speaker 1>and there were six databases in total that were affected

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<v Speaker 1>by this, six massive databases, and the take was huge.

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<v Speaker 1>So the hackers made off of information that related to

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<v Speaker 1>more than four hundred and twelve million customer accounts. The

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<v Speaker 1>information included email addresses, including some belonging to government and

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<v Speaker 1>military users, transaction history, account passwords. Some of these passwords

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<v Speaker 1>at least were encrypted, but they used a really primitive

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<v Speaker 1>hash to do it, an outdated method that was no

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<v Speaker 1>longer considered secure, so that was a big, prible problem.

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<v Speaker 1>More than three hundred million of the accounts came from

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<v Speaker 1>Adult friend Finder, and more than sixty million came from

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<v Speaker 1>a webcam site. And I'm sure a lot of customers

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<v Speaker 1>got really nervous about this. I mean, the taboo nature

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<v Speaker 1>of these sites and services meant a lot of people

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<v Speaker 1>were probably sweating over their past activities and hoping they

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<v Speaker 1>wouldn't be exposed. Now, keep in mind that one year earlier,

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<v Speaker 1>in the summer of twenty fifteen, hackers compromised around thirty

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<v Speaker 1>two million accounts from the company Ashley Madison. Ashley Madison

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<v Speaker 1>was built around the idea of a dating service that

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<v Speaker 1>would let married people secretly find potential partners in order

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<v Speaker 1>to have an affair. There was this sense that some

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<v Speaker 1>sort of hacker anarchist was going to reveal salacious details

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<v Speaker 1>about folks in the wake of these attacks, or that

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<v Speaker 1>at the very least, they would make these details available

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<v Speaker 1>so that anyone who really wanted to sift through all

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<v Speaker 1>the stolen information could dig up whether or not you

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<v Speaker 1>know the neighbor down the street was secretly trying to

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<v Speaker 1>sneak around behind their partners back or whatever, or the

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<v Speaker 1>sexual orientation of people you knew. You could find that

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<v Speaker 1>kind of information out based upon the stuff that had

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<v Speaker 1>been stolen in these sorts of attacks, and depending on

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<v Speaker 1>where you are, that kind of thing can have deadly consequences.

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<v Speaker 1>So the information involved with this data breach was extremely sensitive,

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<v Speaker 1>particularly from a social perspective. I mean, you're not likely

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<v Speaker 1>to come forward and say I was the victim of

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<v Speaker 1>identity theft if it also means you have to cop

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<v Speaker 1>up to something that is socially taboo, like, there's just

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<v Speaker 1>a lot of pressure on you to not come forward.

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<v Speaker 1>That the idea of coming forward is actually worse than

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<v Speaker 1>someone taking advantage of the information they have on you. So,

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<v Speaker 1>while this hack didn't include stuff like credit card information,

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<v Speaker 1>just the fact that names were appearing on these customer

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<v Speaker 1>lists was a huge problem. It could give other hackers

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<v Speaker 1>the opportunity to engage in blackmail or spearfishing and target

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<v Speaker 1>people based on what was revealed in their data with friends.

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<v Speaker 1>And that's a real issue that's going to come up

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<v Speaker 1>again and again in these episodes. Is that idea of yeah,

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<v Speaker 1>the data might not include, say, your credit card, but

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<v Speaker 1>that's not really the concern here. The concern is how

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<v Speaker 1>can someone use your information to victimize you in various ways?

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<v Speaker 1>And one of those is spearfishing. So what did FriendFinder

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<v Speaker 1>Network do in response to this? Sadly, the answer was

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<v Speaker 1>not much. While security researchers alerted the public that they

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<v Speaker 1>had detected a vulnerability in the FriendFinder Network system, the

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<v Speaker 1>company did not acknowledge the data breach for a full

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<v Speaker 1>week and only then began to send out notifications to customers.

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<v Speaker 1>And the company didn't have any really helpful advice for

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<v Speaker 1>those customers either, saying that people should change their passwords. Now,

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<v Speaker 1>according to idstrong dot com, the company had lacks password

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<v Speaker 1>requirements in the first place. Passwords weren't even case sensitive,

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<v Speaker 1>for example, and they didn't update this, so their password

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<v Speaker 1>protocols were still not really at an industry standard. And

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<v Speaker 1>here's a real kicker. The company had also been breached

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<v Speaker 1>in twenty fifteen. Now, the twenty fifteen breach, because remember

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<v Speaker 1>the one we're talking about is really twenty sixteen, But

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<v Speaker 1>the twenty fifteen breach was much smaller in scope. Only

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<v Speaker 1>three and a half million users were affected. That's still

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<v Speaker 1>a lot of people, but it's nowhere close to four

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<v Speaker 1>hundred and twelve million. But the types of information that

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<v Speaker 1>were stolen included things like partial payment information, and at

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<v Speaker 1>least in some of the research I was doing, Like

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<v Speaker 1>some sources said that the types of info that were

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<v Speaker 1>stolen in the twenty sixteen attack did not include things

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<v Speaker 1>like sexual orientation or preferences or that kind of thing.

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<v Speaker 1>Other sources said, no, that was part of the twenty

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<v Speaker 1>sixteen hack as well. So I don't know what the

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<v Speaker 1>full extent was, but a lot of the analysis I've

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<v Speaker 1>looked at about this particular breach points out that the

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<v Speaker 1>company failed to act properly in the wake of the

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<v Speaker 1>twenty fifteen breach, which meant it was essentially set up

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<v Speaker 1>for the much larger attack in twenty sixteen. So that's

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<v Speaker 1>a pretty damning allegation there, right, that a company had

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<v Speaker 1>already been the victim of a massive data breach and

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<v Speaker 1>then failed to take the adequate response in order to

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<v Speaker 1>prevent an even larger data breach the following year. So again,

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<v Speaker 1>just having the basics of your information leaked out would

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<v Speaker 1>be a huge problem given the nature of this company,

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<v Speaker 1>And despite the company's arguably lack luster response to the breach,

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<v Speaker 1>customers kept on being customers. I guess they never had

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<v Speaker 1>to learn a lesson because there really weren't massive consequences.

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<v Speaker 1>And again maybe this is partly because of the nature

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<v Speaker 1>of the services themselves, right, Like for a customer to

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<v Speaker 1>put up a big fuss, they would also have to

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<v Speaker 1>reveal themselves to be a customer in the first place,

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<v Speaker 1>and then the social taboo kicks in again. But unlike

0:13:56.640 --> 0:13:58.480
<v Speaker 1>some other companies that were going to talk about in

0:13:58.480 --> 0:14:02.640
<v Speaker 1>this episode, the friend Finder Networks didn't see serious setbacks

0:14:02.720 --> 0:14:06.400
<v Speaker 1>as a result of this attack. Okay, and we just

0:14:06.440 --> 0:14:09.280
<v Speaker 1>got through one, and we've got lots more to go,

0:14:09.440 --> 0:14:12.040
<v Speaker 1>So let's take a quick break to thank our sponsors

0:14:12.040 --> 0:14:24.280
<v Speaker 1>and we'll be right back. Okay, we're moving on to

0:14:24.360 --> 0:14:27.240
<v Speaker 1>number nine on our list. And this one is a

0:14:27.280 --> 0:14:30.520
<v Speaker 1>real blast from the past. It's MySpace, and this attack

0:14:30.640 --> 0:14:34.600
<v Speaker 1>technically happened in twenty thirteen, but it wasn't discovered and

0:14:34.680 --> 0:14:39.880
<v Speaker 1>reported until twenty sixteen, and even twenty thirteen was late

0:14:39.920 --> 0:14:42.720
<v Speaker 1>in the game for MySpace now. MySpace was once the

0:14:42.920 --> 0:14:46.600
<v Speaker 1>king of social networking platforms, but it had been losing

0:14:46.600 --> 0:14:50.120
<v Speaker 1>ground to Facebook since two thousand and nine. News Corps,

0:14:50.280 --> 0:14:53.080
<v Speaker 1>which had purchased MySpace for a whopping five hundred and

0:14:53.120 --> 0:14:56.160
<v Speaker 1>eighty million dollars in two thousand and five, ended up

0:14:56.280 --> 0:14:59.240
<v Speaker 1>selling the company off to Justin Timberlake and a company

0:14:59.240 --> 0:15:02.920
<v Speaker 1>called Specific Media in twenty eleven for thirty five million dollars.

0:15:02.960 --> 0:15:06.320
<v Speaker 1>So again they purchased it for five hundred eighty million

0:15:06.480 --> 0:15:08.960
<v Speaker 1>and then six years later sold it for thirty five million.

0:15:09.280 --> 0:15:13.520
<v Speaker 1>Not a good deal. By twenty sixteen, Time Incorporated purchased

0:15:13.520 --> 0:15:18.160
<v Speaker 1>Specific Media, and then Meredith Corporation acquired Time Incorporated. Because

0:15:18.160 --> 0:15:20.600
<v Speaker 1>there's always a bigger fish, that story gets more and

0:15:20.600 --> 0:15:22.640
<v Speaker 1>more complicated too, but we're going to leave that here.

0:15:23.200 --> 0:15:26.680
<v Speaker 1>My point is that MySpace had already experienced a dramatic

0:15:26.760 --> 0:15:31.440
<v Speaker 1>decline in relevance by twenty thirteen when the attack actually happened,

0:15:31.640 --> 0:15:35.960
<v Speaker 1>but still the site had millions of user records and

0:15:36.000 --> 0:15:38.600
<v Speaker 1>a hacker was able to get access to them, like

0:15:38.840 --> 0:15:43.720
<v Speaker 1>three hundred and sixty million records. The data lifted during

0:15:43.760 --> 0:15:48.200
<v Speaker 1>the breach included email addresses, user names, and passwords, which

0:15:48.240 --> 0:15:52.040
<v Speaker 1>were encrypted using again an outdated method, and therefore security

0:15:52.040 --> 0:15:55.680
<v Speaker 1>experts considered it insecure, and that was a real issue

0:15:55.760 --> 0:15:58.680
<v Speaker 1>right now. Looking back on this hack today, there's a

0:15:58.720 --> 0:16:02.200
<v Speaker 1>disturbing lack of information as to how it actually happened.

0:16:02.520 --> 0:16:06.000
<v Speaker 1>It went undiscovered for nearly three years and only really

0:16:06.080 --> 0:16:08.440
<v Speaker 1>came to light when folks realized that data from the

0:16:08.480 --> 0:16:11.560
<v Speaker 1>breach was popping up for sale on black market sites

0:16:11.560 --> 0:16:14.400
<v Speaker 1>on the Dark Web. As for who was responsible and

0:16:14.440 --> 0:16:18.640
<v Speaker 1>the vulnerabilities they exploited, that remains something of a mystery.

0:16:19.040 --> 0:16:23.120
<v Speaker 1>MySpace responded to this news by invalidating all the passwords

0:16:23.160 --> 0:16:26.320
<v Speaker 1>of all the affected accounts, which would require users to

0:16:26.360 --> 0:16:29.520
<v Speaker 1>set up new passwords and also encourage people who weren't

0:16:29.560 --> 0:16:32.960
<v Speaker 1>directly impacted to go ahead and update their passwords as well.

0:16:33.040 --> 0:16:36.560
<v Speaker 1>In an overabundance of caution, like the friend Finder breach,

0:16:36.920 --> 0:16:39.760
<v Speaker 1>there wasn't much a user could do to protect themselves

0:16:39.760 --> 0:16:41.720
<v Speaker 1>from the hackers. In fact, I would argue there was

0:16:41.760 --> 0:16:44.720
<v Speaker 1>nothing a user could do. It wouldn't matter if they

0:16:44.800 --> 0:16:47.640
<v Speaker 1>had used a strong or a weak password, because the

0:16:47.720 --> 0:16:50.760
<v Speaker 1>real issue was MySpace was using a very weak hashing

0:16:50.840 --> 0:16:54.040
<v Speaker 1>method to encrypt passwords in the first place. So even

0:16:54.040 --> 0:16:57.120
<v Speaker 1>if you picked a very strong password, if it's being

0:16:57.200 --> 0:17:01.960
<v Speaker 1>stored in an encryption that can easily be broken, then

0:17:02.560 --> 0:17:05.560
<v Speaker 1>they can just get to your password anyway, doesn't matter

0:17:05.560 --> 0:17:09.240
<v Speaker 1>how strong it was. You did your part. MySpace failed,

0:17:09.440 --> 0:17:11.919
<v Speaker 1>is what I'm saying. Now. All that being said, I

0:17:11.960 --> 0:17:15.159
<v Speaker 1>do still urge everyone to use unique, strong passwords for

0:17:15.240 --> 0:17:18.600
<v Speaker 1>all their sites and services. Unique is really important because

0:17:18.600 --> 0:17:21.280
<v Speaker 1>if you're using the same password everywhere, it just takes

0:17:21.400 --> 0:17:24.760
<v Speaker 1>one data breach to be able to compromise all of

0:17:24.800 --> 0:17:27.880
<v Speaker 1>your stuff. If they have your email and whatever password

0:17:27.880 --> 0:17:32.320
<v Speaker 1>you use for that, you know, one like obscure website,

0:17:32.440 --> 0:17:34.400
<v Speaker 1>and it happens to be the same password you use

0:17:34.440 --> 0:17:38.000
<v Speaker 1>for say your bank, that's bad news for you. Use

0:17:38.119 --> 0:17:41.879
<v Speaker 1>unique passwords, get a password vault of some sort a

0:17:41.880 --> 0:17:45.600
<v Speaker 1>good one, research this and find one that really works

0:17:45.640 --> 0:17:49.560
<v Speaker 1>for you, and make unique, strong passwords for each of

0:17:49.600 --> 0:17:52.840
<v Speaker 1>the sites you go to so that you can avoid

0:17:53.119 --> 0:17:56.360
<v Speaker 1>this issue. Because data breaches, sadly are not uncommon, they're

0:17:56.359 --> 0:18:00.560
<v Speaker 1>getting more common every year, and this will help protect

0:18:00.760 --> 0:18:05.280
<v Speaker 1>other elements of your online presence from hackers. Sadly, there's

0:18:05.280 --> 0:18:08.040
<v Speaker 1>not very much you can do to protect the systems themselves.

0:18:08.080 --> 0:18:11.000
<v Speaker 1>I mean, that's in the control of whatever platform you're using.

0:18:11.040 --> 0:18:13.640
<v Speaker 1>And I'm not telling you not to use platforms goodness, nos,

0:18:13.720 --> 0:18:17.080
<v Speaker 1>I use tons of them. Just to be as careful

0:18:17.080 --> 0:18:19.960
<v Speaker 1>as you can be to mitigate any issues that might

0:18:20.000 --> 0:18:23.280
<v Speaker 1>pop up due to data breaches. Also, you know, enable

0:18:23.359 --> 0:18:26.800
<v Speaker 1>multi factor authentication if that's available, if it's on there,

0:18:27.280 --> 0:18:31.000
<v Speaker 1>use it again. Nothing is absolutely fool proof. I'm not

0:18:31.080 --> 0:18:33.159
<v Speaker 1>here to tell you that if you have multi factor

0:18:33.200 --> 0:18:37.600
<v Speaker 1>authentication you'll never get hacked. That's not necessarily true. But

0:18:37.680 --> 0:18:41.239
<v Speaker 1>the more precautions you take the better. The harder you

0:18:41.320 --> 0:18:44.760
<v Speaker 1>make yourself to be a target, the more effort it

0:18:44.800 --> 0:18:49.359
<v Speaker 1>takes to actually crack your security, and the less likely

0:18:49.440 --> 0:18:54.399
<v Speaker 1>someone's going to actually pursue that. It's not impossible, but like,

0:18:54.840 --> 0:18:57.360
<v Speaker 1>why struggle if you can go for all the low

0:18:57.400 --> 0:19:00.400
<v Speaker 1>hanging fruit, don't be low hanging fruit still. Now, if

0:19:00.400 --> 0:19:03.960
<v Speaker 1>hackers are breaching a company's systems, we're really left to

0:19:03.960 --> 0:19:07.399
<v Speaker 1>the competence of that company when it comes to personal security.

0:19:07.680 --> 0:19:09.680
<v Speaker 1>So our first two entries on this list are both

0:19:09.960 --> 0:19:13.960
<v Speaker 1>web based companies. Right, we had MySpace and we had

0:19:14.119 --> 0:19:17.520
<v Speaker 1>the FriendFinder Networks. But up next is a company known

0:19:17.560 --> 0:19:20.840
<v Speaker 1>for its brick and mortar operations, and I'm talking about

0:19:21.040 --> 0:19:25.199
<v Speaker 1>home Depot, which experienced a massive data breach in April

0:19:25.240 --> 0:19:28.920
<v Speaker 1>twenty fourteen. This was an attack that compromised more than

0:19:28.960 --> 0:19:35.040
<v Speaker 1>fifty million customers data, including their credit or debit card information,

0:19:35.600 --> 0:19:39.959
<v Speaker 1>lifting that information right from inside the stores themselves. And

0:19:40.000 --> 0:19:42.639
<v Speaker 1>this attack went unnoticed until the hackers started putting the

0:19:42.640 --> 0:19:45.240
<v Speaker 1>credit card info up on sale on the dark web,

0:19:45.440 --> 0:19:48.680
<v Speaker 1>at which point home Depot was made aware that they

0:19:48.760 --> 0:19:52.399
<v Speaker 1>had been breached. So let's walk through how this attack happened. So,

0:19:52.480 --> 0:19:56.240
<v Speaker 1>according to the US Office of the Director of National Intelligence,

0:19:56.480 --> 0:20:01.000
<v Speaker 1>the hackers first secured Quote credentials, user names, and passwords

0:20:01.280 --> 0:20:04.720
<v Speaker 1>from a third party vendor end Quote, and that gave

0:20:04.760 --> 0:20:08.840
<v Speaker 1>them the foothold into home depots computer network. So first

0:20:09.040 --> 0:20:12.000
<v Speaker 1>they identified a company that worked with home Depot. They

0:20:12.000 --> 0:20:15.640
<v Speaker 1>were able to secure a username and password from this company.

0:20:15.880 --> 0:20:19.719
<v Speaker 1>They use that to infiltrate home Depot's computer network. On

0:20:19.800 --> 0:20:25.200
<v Speaker 1>top of that, they then were able to essentially take

0:20:25.200 --> 0:20:29.720
<v Speaker 1>advantage of a zero day vulnerability that was within Microsoft Windows.

0:20:29.840 --> 0:20:32.399
<v Speaker 1>So a zero day vulnerability is a fancy way of

0:20:32.440 --> 0:20:36.080
<v Speaker 1>saying that the entity responsible for making whatever the thing

0:20:36.240 --> 0:20:39.800
<v Speaker 1>is So in this case, Microsoft Windows is unaware that

0:20:39.800 --> 0:20:43.720
<v Speaker 1>the vulnerability even exists. And because they're unaware that there

0:20:43.800 --> 0:20:47.040
<v Speaker 1>is a vulnerability, there's no means to prevent or mitigate

0:20:47.160 --> 0:20:51.920
<v Speaker 1>attacks that leverage or exploit this vulnerability. Zero day vulnerabilities

0:20:51.960 --> 0:20:56.159
<v Speaker 1>are incredibly valuable in the hacker community because there's no

0:20:56.280 --> 0:20:59.879
<v Speaker 1>real defense against them, and if you're very careful, you

0:21:00.560 --> 0:21:04.000
<v Speaker 1>have the chance to continue to exploit these kinds of

0:21:04.040 --> 0:21:07.680
<v Speaker 1>vulnerabilities for a while before anyone notices. So it's called

0:21:07.800 --> 0:21:10.439
<v Speaker 1>zero day because that's how much time the you know,

0:21:10.480 --> 0:21:14.320
<v Speaker 1>the entity Microsoft in this case has before malicious agents

0:21:14.359 --> 0:21:18.160
<v Speaker 1>are able to exploit that vulnerability. So the hackers exploit

0:21:18.320 --> 0:21:22.600
<v Speaker 1>Microsoft Windows and they're exploring home Depot systems and they're

0:21:22.600 --> 0:21:27.080
<v Speaker 1>able to identify thousands, like seven five hundred points of

0:21:27.200 --> 0:21:31.840
<v Speaker 1>sale systems in self checkout lanes at physical home Depot stores.

0:21:31.880 --> 0:21:35.359
<v Speaker 1>So again, this was not targeting the online point of

0:21:35.400 --> 0:21:38.800
<v Speaker 1>sale operations for home Depot. You know, the website commerce

0:21:39.040 --> 0:21:41.720
<v Speaker 1>part of Home Depot was not part of this attack,

0:21:42.000 --> 0:21:43.960
<v Speaker 1>And I just think that's good to point out because

0:21:44.160 --> 0:21:46.480
<v Speaker 1>I don't think it's as common now, But I remember

0:21:46.560 --> 0:21:51.000
<v Speaker 1>when online commerce first became a thing, people were scared

0:21:51.520 --> 0:21:54.639
<v Speaker 1>to buy stuff off the internet. They were reluctant to

0:21:54.760 --> 0:21:57.439
<v Speaker 1>use their credit card to purchase something online because they

0:21:57.480 --> 0:22:01.040
<v Speaker 1>were worried about security, which is understandable, but it turns

0:22:01.080 --> 0:22:04.000
<v Speaker 1>out that going to a brick and mortar store is

0:22:04.000 --> 0:22:08.320
<v Speaker 1>not necessarily more secure because those systems are also connected

0:22:08.359 --> 0:22:11.480
<v Speaker 1>to networks that ultimately get connected to the Internet, and

0:22:11.560 --> 0:22:15.159
<v Speaker 1>so if you're able to compromise those networks, then you

0:22:15.160 --> 0:22:18.320
<v Speaker 1>can still tap into that kind of system. So the

0:22:18.359 --> 0:22:22.400
<v Speaker 1>hackers deployed custom built malware for these points of sale systems,

0:22:22.480 --> 0:22:25.040
<v Speaker 1>and they use this malware to record the credit and

0:22:25.119 --> 0:22:28.360
<v Speaker 1>debit card information of home Depot customers. They even made

0:22:28.400 --> 0:22:32.360
<v Speaker 1>sure that they transmitted that data during home Depot's business

0:22:32.400 --> 0:22:36.080
<v Speaker 1>hours so that the company's security team wouldn't notice like

0:22:36.760 --> 0:22:39.399
<v Speaker 1>a transmission at an odd hour, like if it was

0:22:39.440 --> 0:22:42.120
<v Speaker 1>two in the morning, then the security team was saying, like, hey,

0:22:42.160 --> 0:22:45.240
<v Speaker 1>why is our system sending info out at this hour?

0:22:45.600 --> 0:22:47.400
<v Speaker 1>That could be a tip off. So they made sure

0:22:47.440 --> 0:22:51.479
<v Speaker 1>that all those transmissions happened during normal business operating hours

0:22:51.760 --> 0:22:55.280
<v Speaker 1>and that would kind of mask these On top of

0:22:55.359 --> 0:23:00.080
<v Speaker 1>all the legitimate transmissions, cybersecurity experts criticized home Depot so

0:23:00.320 --> 0:23:03.960
<v Speaker 1>for having insufficient security measures in place. The company estimated

0:23:03.960 --> 0:23:07.359
<v Speaker 1>that spent nearly one hundred and eighty million dollars in

0:23:07.400 --> 0:23:09.560
<v Speaker 1>the wake of this attack to pay off all the

0:23:09.680 --> 0:23:12.280
<v Speaker 1>various costs. On top of that, there was a class

0:23:12.320 --> 0:23:15.840
<v Speaker 1>action lawsuit from across forty six states that ended with

0:23:15.920 --> 0:23:18.920
<v Speaker 1>Home Depots settling out of court for seventeen point five

0:23:19.040 --> 0:23:24.399
<v Speaker 1>million dollars. Now, Home Depot didn't admit, you know, responsibility

0:23:24.680 --> 0:23:27.720
<v Speaker 1>for this, but it did promise to invest in security measures,

0:23:27.720 --> 0:23:30.880
<v Speaker 1>including hiring a chief of information of security. Now, as

0:23:30.920 --> 0:23:33.439
<v Speaker 1>for that seventeen point five million dollar settlement, I just

0:23:33.480 --> 0:23:36.000
<v Speaker 1>want to put that into context so that we can

0:23:36.080 --> 0:23:39.800
<v Speaker 1>kind of appreciate what that means or doesn't mean. Keep

0:23:39.840 --> 0:23:43.520
<v Speaker 1>in mind, around fifty six million customers were affected by

0:23:43.520 --> 0:23:46.080
<v Speaker 1>this data breach, So if you were to include all

0:23:46.119 --> 0:23:48.840
<v Speaker 1>of them in the class action lawsuit, which obviously not

0:23:48.880 --> 0:23:50.840
<v Speaker 1>realistic but you know, we're just doing this as a

0:23:50.880 --> 0:23:54.520
<v Speaker 1>thought experiment, then that would mean each person would receive

0:23:54.600 --> 0:23:58.800
<v Speaker 1>the princely sum of thirty one cents. That's only if

0:23:58.840 --> 0:24:02.320
<v Speaker 1>the various lawyers of all the different states did this case.

0:24:02.520 --> 0:24:05.480
<v Speaker 1>Gradis for free. So what I'm saying is that while

0:24:05.480 --> 0:24:07.200
<v Speaker 1>Home Depot may have had to spend a lot of

0:24:07.280 --> 0:24:09.960
<v Speaker 1>money to deal with the aftermath of this breach, the

0:24:10.040 --> 0:24:13.240
<v Speaker 1>settlement I think was a case of getting off lightly

0:24:13.440 --> 0:24:16.679
<v Speaker 1>considering the nature of that breach. But I also have

0:24:16.760 --> 0:24:19.879
<v Speaker 1>to remind myself that ultimately the real criminal here are

0:24:19.920 --> 0:24:22.600
<v Speaker 1>the hackers who pulled off the attack and the folks

0:24:22.640 --> 0:24:25.159
<v Speaker 1>on the dark web who purchased the credit and debit

0:24:25.200 --> 0:24:27.959
<v Speaker 1>card information. Those are the real criminals. While I can

0:24:28.000 --> 0:24:33.720
<v Speaker 1>be disappointed in home Depot's lack of security or lackluster security,

0:24:33.720 --> 0:24:36.400
<v Speaker 1>in this case, I don't want to blame the victim

0:24:36.840 --> 0:24:39.240
<v Speaker 1>like I do think that there is a responsibility there,

0:24:39.280 --> 0:24:42.840
<v Speaker 1>But the real villains are the people who did the stealing.

0:24:43.119 --> 0:24:45.560
<v Speaker 1>It's just it's easy to blame big companies as well

0:24:45.560 --> 0:24:48.240
<v Speaker 1>when they failed to be good stewards of customer information.

0:24:48.840 --> 0:24:51.879
<v Speaker 1>So next up on Chin's list, oh massive data breaches

0:24:51.880 --> 0:24:53.720
<v Speaker 1>here in the United States, is another one that happened

0:24:53.720 --> 0:24:58.080
<v Speaker 1>in twenty fourteen. This attack targeted the bank JP Morgan

0:24:58.240 --> 0:25:01.960
<v Speaker 1>Chase and it impacted around eighty three million bank customers.

0:25:02.160 --> 0:25:05.000
<v Speaker 1>Seventy six million of those were households and the other

0:25:05.040 --> 0:25:10.640
<v Speaker 1>seven million were small businesses. This attack also reportedly leveraged

0:25:10.680 --> 0:25:13.280
<v Speaker 1>a zero day vulnerability, but in this case, it was

0:25:13.280 --> 0:25:17.479
<v Speaker 1>a vulnerability in JP Morgan Chase's web applications, so this

0:25:17.640 --> 0:25:21.399
<v Speaker 1>gave the hackers the foothold to access kind of a

0:25:21.440 --> 0:25:25.800
<v Speaker 1>directory level of server information for JP Morgan Chase. This

0:25:25.880 --> 0:25:31.040
<v Speaker 1>then let the hackers identify databases containing customer information Now,

0:25:31.600 --> 0:25:34.960
<v Speaker 1>one source I looked at suggested the information included financial

0:25:35.040 --> 0:25:37.479
<v Speaker 1>data like credit card information, but that was just in

0:25:37.560 --> 0:25:41.120
<v Speaker 1>one source, and every other source, including The New York Times,

0:25:41.359 --> 0:25:43.919
<v Speaker 1>says that was not the case. So I feel pretty

0:25:43.960 --> 0:25:47.960
<v Speaker 1>confident that that one source was an outlier and had

0:25:48.000 --> 0:25:50.359
<v Speaker 1>some misinformation in it. I mean, that's a flag for

0:25:50.440 --> 0:25:52.359
<v Speaker 1>all of y'all out there. So it's always good to

0:25:52.560 --> 0:25:56.320
<v Speaker 1>double check things and check multiple sources. Sometimes it can

0:25:56.400 --> 0:26:00.800
<v Speaker 1>be really difficult to determine what reality is based on

0:26:01.440 --> 0:26:06.360
<v Speaker 1>the reporting of various sources. Sometimes even reputable sources get

0:26:06.400 --> 0:26:10.720
<v Speaker 1>things wrong. So you know, thinking critically involves a lot

0:26:10.760 --> 0:26:13.960
<v Speaker 1>of checking and double checking, and sometimes it involves making

0:26:14.240 --> 0:26:16.960
<v Speaker 1>an educated guess as to what is most likely to

0:26:16.960 --> 0:26:18.879
<v Speaker 1>be real. So in this case, I think it's most

0:26:18.920 --> 0:26:22.080
<v Speaker 1>likely that the information that was stolen was personal information

0:26:22.320 --> 0:26:25.600
<v Speaker 1>but not financial information. So the attackers got access to

0:26:25.720 --> 0:26:28.640
<v Speaker 1>things like names, email addresses, that kind of thing, which

0:26:28.680 --> 0:26:31.640
<v Speaker 1>again doesn't sound like it's as critical as credit card information,

0:26:31.880 --> 0:26:34.840
<v Speaker 1>but it's still really useful data if, for example, you

0:26:34.880 --> 0:26:37.800
<v Speaker 1>want to create a spear phishing campaign and trick people

0:26:37.840 --> 0:26:41.000
<v Speaker 1>into making mistakes, like if you know they are customers

0:26:41.040 --> 0:26:44.320
<v Speaker 1>of this particular bank, and you know what their email

0:26:44.359 --> 0:26:47.240
<v Speaker 1>address is, and you know their actual name, you can

0:26:47.320 --> 0:26:50.919
<v Speaker 1>craft and attack targeting that person that appears to be

0:26:51.000 --> 0:26:54.760
<v Speaker 1>coming from the legitimate business and potentially take advantage of

0:26:54.800 --> 0:26:58.119
<v Speaker 1>them that way. So the hackers then developed attacks for

0:26:58.240 --> 0:27:02.080
<v Speaker 1>these servers they had identified, and they ultimately infiltrated around

0:27:02.240 --> 0:27:07.080
<v Speaker 1>ninety servers within the business. The attackers had started back

0:27:07.080 --> 0:27:10.959
<v Speaker 1>in June twenty fourteen. JP Morgan Chase would detect the

0:27:10.960 --> 0:27:14.480
<v Speaker 1>intrusion a month later in July. The public, however, would

0:27:14.480 --> 0:27:17.800
<v Speaker 1>not find out about it until September, when the company

0:27:17.800 --> 0:27:20.960
<v Speaker 1>disclosed the attack in a securities filing and various media

0:27:21.000 --> 0:27:24.520
<v Speaker 1>outlets reported on it. Now, considering that other major breaches

0:27:24.600 --> 0:27:27.520
<v Speaker 1>like the aforementioned home depot attack, there was another one

0:27:27.520 --> 0:27:30.760
<v Speaker 1>that hit target, these attacks were fresh in the minds

0:27:30.760 --> 0:27:32.920
<v Speaker 1>of consumers because they were national news here in the

0:27:33.000 --> 0:27:36.359
<v Speaker 1>United States. The JP Morgan Chase attack was a huge

0:27:36.400 --> 0:27:41.480
<v Speaker 1>blow because it revealed that even massive financial institutions, which

0:27:41.560 --> 0:27:45.520
<v Speaker 1>had good reputations for being really secure, could also fall

0:27:45.640 --> 0:27:49.520
<v Speaker 1>victim to hacker intrusions, which became a brand news source

0:27:49.560 --> 0:27:53.560
<v Speaker 1>for anxiety for American consumers and as for the attackers

0:27:53.560 --> 0:27:57.280
<v Speaker 1>in this case, there were four identified arguably five. The

0:27:57.320 --> 0:28:00.199
<v Speaker 1>fifth one, however, was kind of after the effect, but

0:28:00.240 --> 0:28:04.280
<v Speaker 1>the main four included a Russian citizen named Andrew Turin.

0:28:04.560 --> 0:28:10.240
<v Speaker 1>There was an American named Joshua Samuel Arn aka Mike Shields.

0:28:10.560 --> 0:28:13.080
<v Speaker 1>That's the alias he would use and some of his

0:28:13.200 --> 0:28:16.560
<v Speaker 1>nefarious activities according to authorities. And then there were two

0:28:16.640 --> 0:28:23.840
<v Speaker 1>Israeli citizens. There was Gary Shalan aka Gary Shallis Lashville.

0:28:24.160 --> 0:28:29.200
<v Speaker 1>I know I mangled that name aka Gabriel aka Gabby

0:28:29.520 --> 0:28:36.199
<v Speaker 1>aka Philip Moussey aka Christopher Ingeham. Lots of aliases for

0:28:36.359 --> 0:28:40.800
<v Speaker 1>Gary Shallon. And then finally there was Ziv Ornstein aka

0:28:41.080 --> 0:28:46.280
<v Speaker 1>Aviv Stein aka John Avery. So for four people, that's

0:28:46.320 --> 0:28:48.720
<v Speaker 1>a lot of different names, right. Well, these four hackers

0:28:48.720 --> 0:28:52.320
<v Speaker 1>were linked to numerous crimes, not just the JP Morgan

0:28:52.520 --> 0:28:55.360
<v Speaker 1>chase instance. There were other ones as well, and they

0:28:55.360 --> 0:28:59.240
<v Speaker 1>were also operators I believe of online casino or something

0:28:59.280 --> 0:29:01.400
<v Speaker 1>along those lines. Anyway, at least one of them, that

0:29:01.560 --> 0:29:05.680
<v Speaker 1>being Gary Shallon, was released early. He secured an early

0:29:05.720 --> 0:29:08.680
<v Speaker 1>release after agreeing to a plea deal that had him

0:29:08.720 --> 0:29:13.320
<v Speaker 1>pay a whopping four hundred three million dollar fine. Now,

0:29:13.320 --> 0:29:15.840
<v Speaker 1>if you can afford to pay a four hundred three

0:29:15.920 --> 0:29:18.400
<v Speaker 1>million dollar fine to get out of the pokey. I mean,

0:29:18.440 --> 0:29:21.280
<v Speaker 1>I guess crime really does pay. Other folks connected to

0:29:21.320 --> 0:29:23.920
<v Speaker 1>the scheme were not so fortunate, so for example, Andrew

0:29:24.000 --> 0:29:27.080
<v Speaker 1>Tieran received a twelve year sentence at the end of

0:29:27.080 --> 0:29:30.400
<v Speaker 1>his trial. So I guess it's you know who you know,

0:29:30.880 --> 0:29:33.080
<v Speaker 1>and who you know needs to be a whole lot

0:29:33.080 --> 0:29:36.680
<v Speaker 1>of Benjamin Franklin's JP Morgan Chase pledged to beef up

0:29:36.680 --> 0:29:40.000
<v Speaker 1>the company's security and would double the investment within five

0:29:40.080 --> 0:29:42.240
<v Speaker 1>years from two hundred and fifty million a year to

0:29:42.480 --> 0:29:46.080
<v Speaker 1>five hundred million a year. So that's good. Okay, got

0:29:46.080 --> 0:29:48.000
<v Speaker 1>a couple more I want to talk about before we

0:29:48.040 --> 0:29:51.280
<v Speaker 1>wrap up Part one. I guess of our top ten

0:29:51.960 --> 0:29:55.480
<v Speaker 1>largest data breaches in US history, But first let's take

0:29:55.480 --> 0:30:08.040
<v Speaker 1>another quick break to thank our sponsors. We are up

0:30:08.120 --> 0:30:11.640
<v Speaker 1>to number six on our list of biggest data breaches

0:30:11.680 --> 0:30:16.560
<v Speaker 1>in US history. And that would be LinkedIn. Uh, LinkedIn,

0:30:16.680 --> 0:30:20.200
<v Speaker 1>that social network site that I almost never log into.

0:30:20.760 --> 0:30:24.560
<v Speaker 1>If I were a savvy mover and shaker, I would

0:30:24.600 --> 0:30:28.479
<v Speaker 1>make way better use of LinkedIn, But I'm not, and

0:30:28.560 --> 0:30:31.280
<v Speaker 1>so I post to my account once every blue moon,

0:30:31.600 --> 0:30:33.640
<v Speaker 1>and I keep thinking, Man, I need to make better

0:30:33.720 --> 0:30:36.080
<v Speaker 1>use of this resource and really network with people. That

0:30:36.120 --> 0:30:38.800
<v Speaker 1>could be so helpful. But I've got only so much

0:30:39.720 --> 0:30:43.120
<v Speaker 1>emotional energy for things like social networks. And I still

0:30:43.440 --> 0:30:46.360
<v Speaker 1>have a LinkedIn account. I just don't use it very much. However,

0:30:46.480 --> 0:30:49.840
<v Speaker 1>because I have a LinkedIn account, this next story affects

0:30:49.840 --> 0:30:52.840
<v Speaker 1>me whether I pop on there regularly or not. This

0:30:52.960 --> 0:30:56.240
<v Speaker 1>data breach is quite a bit different from the ones

0:30:56.280 --> 0:30:58.920
<v Speaker 1>we've talked about so far because this one did not

0:30:59.120 --> 0:31:04.480
<v Speaker 1>involve a HA gaining access to LinkedIn's internal systems. There

0:31:04.520 --> 0:31:08.600
<v Speaker 1>was no security intrusion in this case. Instead, the hacker

0:31:09.200 --> 0:31:12.120
<v Speaker 1>someone at least what's believed is that was a hacker

0:31:12.200 --> 0:31:16.880
<v Speaker 1>using the handle Tomliner, but Tomliner could be a middleman

0:31:17.080 --> 0:31:20.240
<v Speaker 1>like he might not he or she or they might

0:31:20.280 --> 0:31:22.960
<v Speaker 1>not have been the person responsible for the actual hack,

0:31:23.000 --> 0:31:25.280
<v Speaker 1>but they did get access to at least some of

0:31:25.320 --> 0:31:29.200
<v Speaker 1>the data. Anyway, The quote unquote hacker simply used tools

0:31:29.280 --> 0:31:33.360
<v Speaker 1>to scrape data off public profiles on LinkedIn. A ton

0:31:33.400 --> 0:31:36.960
<v Speaker 1>of public profiles, like more than ninety percent of the

0:31:37.040 --> 0:31:40.479
<v Speaker 1>public profiles on LinkedIn. That would be around seven hundred

0:31:40.720 --> 0:31:45.160
<v Speaker 1>million profiles. And here's the crazy thing. Earlier that same year,

0:31:45.640 --> 0:31:50.280
<v Speaker 1>the same person claimed responsibility for leaking five hundred million

0:31:50.360 --> 0:31:53.560
<v Speaker 1>LinkedIn records, So this was like the second time in

0:31:53.600 --> 0:31:55.800
<v Speaker 1>the same year and going from five hundred million to

0:31:55.880 --> 0:32:00.400
<v Speaker 1>seven hundred million yaalza. Now, essentially this methodology is the

0:32:00.440 --> 0:32:03.800
<v Speaker 1>same as if you were to go manually from LinkedIn

0:32:03.920 --> 0:32:07.080
<v Speaker 1>profile to profile and you just jotted down all the

0:32:07.120 --> 0:32:09.800
<v Speaker 1>relevant information that you were looking for. You know, stuff

0:32:09.880 --> 0:32:13.480
<v Speaker 1>like what's a person's username, what's their full name, what's

0:32:13.520 --> 0:32:17.320
<v Speaker 1>their phone number, their email address, you know what other

0:32:17.440 --> 0:32:21.479
<v Speaker 1>social networking sites do they use? Anything that would appear

0:32:21.600 --> 0:32:24.360
<v Speaker 1>on the person's profile. You would just jot it down.

0:32:24.680 --> 0:32:28.040
<v Speaker 1>That would take you an eternity to do seven hundred million,

0:32:28.400 --> 0:32:32.360
<v Speaker 1>So you create a tool that will just do this automatically.

0:32:32.600 --> 0:32:35.760
<v Speaker 1>So the hacker had used LinkedIn's API that stands for

0:32:35.880 --> 0:32:41.000
<v Speaker 1>Application Programmer Interface and they designed these data scraping tools

0:32:41.040 --> 0:32:45.960
<v Speaker 1>to harvest user data. This was against LinkedIn's policies, but

0:32:46.040 --> 0:32:49.120
<v Speaker 1>there really weren't any measures in place to actually prevent

0:32:49.160 --> 0:32:52.120
<v Speaker 1>it from happening. So yeah, LinkedIn says, hey, don't do this,

0:32:52.320 --> 0:32:54.200
<v Speaker 1>but they didn't have a way to stop you from

0:32:54.240 --> 0:32:57.200
<v Speaker 1>doing it twice in the same year. As it turns out,

0:32:57.480 --> 0:33:01.720
<v Speaker 1>now this attack did not compromise stuff like passwords or

0:33:01.760 --> 0:33:05.600
<v Speaker 1>financial information, but it did include things like those connected

0:33:05.760 --> 0:33:09.360
<v Speaker 1>social applications. So if an affected user had linked their

0:33:09.400 --> 0:33:12.920
<v Speaker 1>Facebook account or whatever to their LinkedIn profile, that meant

0:33:12.960 --> 0:33:15.920
<v Speaker 1>the attackers would have that information. And again this can

0:33:15.960 --> 0:33:19.040
<v Speaker 1>be incredibly helpful if you want to design a phishing attack.

0:33:19.360 --> 0:33:22.160
<v Speaker 1>You know, your basic blunt phishing attack might start from

0:33:22.160 --> 0:33:25.000
<v Speaker 1>a place where little to nothing is known about your target.

0:33:25.280 --> 0:33:28.360
<v Speaker 1>But the more attackers learn about you, the better they

0:33:28.360 --> 0:33:31.640
<v Speaker 1>can craft an effective trap. And considering that there were

0:33:31.760 --> 0:33:35.000
<v Speaker 1>a lot of executives using LinkedIn to network with each other,

0:33:35.280 --> 0:33:38.920
<v Speaker 1>there's some really high value targets mixed in with everybody else.

0:33:39.440 --> 0:33:41.880
<v Speaker 1>Like even if it's not an executive, it might be

0:33:41.960 --> 0:33:45.640
<v Speaker 1>someone who's an associate of an executive, like an assistant

0:33:45.840 --> 0:33:48.440
<v Speaker 1>or a coworker or something like that, a direct report.

0:33:48.680 --> 0:33:51.680
<v Speaker 1>And if you're able to know who that person's direct

0:33:51.720 --> 0:33:54.400
<v Speaker 1>report is or who they're reporting to, I guess I

0:33:54.440 --> 0:33:57.480
<v Speaker 1>should say, then you can craft an attack that might

0:33:57.520 --> 0:34:01.080
<v Speaker 1>be very convincing. You know, a classic one is your

0:34:01.120 --> 0:34:04.880
<v Speaker 1>boss apparently texting you out of nowhere saying hey, I

0:34:05.000 --> 0:34:08.920
<v Speaker 1>need access to five thousand dollars in petty cash. Can

0:34:08.960 --> 0:34:10.759
<v Speaker 1>you wire it to me, and then they give you

0:34:10.800 --> 0:34:12.800
<v Speaker 1>a link and it turns out it's just someone who's

0:34:13.680 --> 0:34:16.600
<v Speaker 1>made the connection. They know who your boss is, and

0:34:16.640 --> 0:34:19.480
<v Speaker 1>they're using that to pressure you into doing something you

0:34:19.560 --> 0:34:22.239
<v Speaker 1>really shouldn't do. That's a very simple example, but it

0:34:22.280 --> 0:34:25.040
<v Speaker 1>happens all the time. So this LinkedIn attack is a

0:34:25.040 --> 0:34:28.680
<v Speaker 1>pretty tricky one. And we've seen similar data scraping techniques

0:34:28.760 --> 0:34:31.920
<v Speaker 1>across the web, both of the purposes of harvesting user

0:34:31.960 --> 0:34:35.200
<v Speaker 1>information and in recent years also using it to train

0:34:35.320 --> 0:34:40.480
<v Speaker 1>up AI models. And typically platforms condemn these practices. They

0:34:40.520 --> 0:34:43.239
<v Speaker 1>say it violates their policies. They want to protect their

0:34:43.320 --> 0:34:47.320
<v Speaker 1>user information. Now, I would argue it's largely really because

0:34:47.920 --> 0:34:51.279
<v Speaker 1>user data is valuable, and these platforms would very much

0:34:51.360 --> 0:34:54.160
<v Speaker 1>like to prevent other entities from taking advantage of the

0:34:54.200 --> 0:34:57.960
<v Speaker 1>same information that the platforms themselves are profiting off of.

0:34:58.400 --> 0:35:01.080
<v Speaker 1>It's not so much to protect our privacy as it

0:35:01.120 --> 0:35:04.880
<v Speaker 1>is to protect the platform's investment in gathering all the

0:35:04.920 --> 0:35:07.360
<v Speaker 1>information in the first place. Like no, this is ours.

0:35:07.719 --> 0:35:12.160
<v Speaker 1>This is ours to exploit and to profit from, not yours. Well.

0:35:12.239 --> 0:35:15.040
<v Speaker 1>Number five on this list includes an old topic for

0:35:15.160 --> 0:35:20.239
<v Speaker 1>tech stuff, which is the infamous Cambridge Analytica case with Facebook. Now,

0:35:20.280 --> 0:35:22.759
<v Speaker 1>this one is a little bit complicated, but I'll see

0:35:22.760 --> 0:35:25.680
<v Speaker 1>if I can summarize at least the tech side of it,

0:35:25.680 --> 0:35:30.160
<v Speaker 1>although it does also include politics. Sorry I wish it didn't,

0:35:30.480 --> 0:35:34.560
<v Speaker 1>but it's literally the very nature of this case. So

0:35:34.960 --> 0:35:37.080
<v Speaker 1>the LinkedIn attack we just talked about is kind of

0:35:37.120 --> 0:35:41.440
<v Speaker 1>similar to this because this attack, the Cambridge Analytica scandal,

0:35:41.640 --> 0:35:47.160
<v Speaker 1>really centers on some loopholes in Facebook's API. So it

0:35:47.200 --> 0:35:51.640
<v Speaker 1>all starts with a researcher named Alexander Cogan. And Cogan

0:35:51.719 --> 0:35:54.920
<v Speaker 1>used Facebook's API to create a survey app, and it

0:35:54.960 --> 0:35:57.960
<v Speaker 1>would pay Facebook users a small amount in return for

0:35:58.040 --> 0:36:01.279
<v Speaker 1>them taking the survey. They did not know is that

0:36:01.360 --> 0:36:05.080
<v Speaker 1>anyone who opted to take this survey was unknowingly giving

0:36:05.160 --> 0:36:09.120
<v Speaker 1>Cogan the ability to view that person's friends profiles as

0:36:09.160 --> 0:36:12.200
<v Speaker 1>if Cogan were in fact the person taking the survey.

0:36:12.440 --> 0:36:14.080
<v Speaker 1>So let me give an example to make this a

0:36:14.080 --> 0:36:17.640
<v Speaker 1>little more clear. Let's say I'm your friend on Facebook.

0:36:17.800 --> 0:36:21.600
<v Speaker 1>Hi friend, and as your friend, I can see more

0:36:21.600 --> 0:36:25.120
<v Speaker 1>of your profile than just some random schmo on the internet. Right,

0:36:25.200 --> 0:36:28.640
<v Speaker 1>maybe you've set certain things on your profile to friends only,

0:36:28.920 --> 0:36:32.360
<v Speaker 1>so as your friend I can see that. But some

0:36:32.560 --> 0:36:35.319
<v Speaker 1>random person wouldn't be able to see that, right, But

0:36:35.400 --> 0:36:37.360
<v Speaker 1>then I decide I'm going to go take the survey

0:36:37.400 --> 0:36:39.719
<v Speaker 1>so I can make twenty bucks or whatever. And now

0:36:39.800 --> 0:36:43.080
<v Speaker 1>Cogan can see your profile as if he were me

0:36:43.760 --> 0:36:47.200
<v Speaker 1>because of this loophole and Facebook's API, and so now

0:36:47.280 --> 0:36:50.719
<v Speaker 1>Cogan can view all of your friend's only information as

0:36:50.760 --> 0:36:54.080
<v Speaker 1>if Cogan were your friend. So Facebook would actually close

0:36:54.120 --> 0:36:58.360
<v Speaker 1>off this loophole before the Cambridge Analytica scandal became ann

0:36:58.480 --> 0:37:01.759
<v Speaker 1>thing like face. This book made that change in the

0:37:01.840 --> 0:37:06.600
<v Speaker 1>years following twenty thirteen when Cogan did this actual work.

0:37:07.480 --> 0:37:11.600
<v Speaker 1>But by then the data already existed with Cogan. Cogan

0:37:11.640 --> 0:37:13.880
<v Speaker 1>had access to all this information and he worked with

0:37:13.920 --> 0:37:17.319
<v Speaker 1>Cambridge Analytica to share it. And so Cambridge Analytica had

0:37:17.360 --> 0:37:20.719
<v Speaker 1>access to all this data they shouldn't have. They did

0:37:20.719 --> 0:37:24.600
<v Speaker 1>not have the consent of the various people on Facebook

0:37:24.640 --> 0:37:27.239
<v Speaker 1>to share the information, and they began to use this

0:37:27.440 --> 0:37:31.960
<v Speaker 1>data in various ways during political campaigns, mostly conservative ones.

0:37:31.960 --> 0:37:35.560
<v Speaker 1>Cambridge Analytica was a British company. It was a sort

0:37:35.560 --> 0:37:39.239
<v Speaker 1>of a campaign strategy company, and their pitch was that

0:37:39.280 --> 0:37:43.359
<v Speaker 1>they were using data driven techniques to make it far

0:37:43.400 --> 0:37:47.040
<v Speaker 1>more effective to get messaging out to potential voters, and

0:37:47.440 --> 0:37:53.360
<v Speaker 1>it was largely for conservative politicians. Facebook was reportedly aware

0:37:53.800 --> 0:37:57.440
<v Speaker 1>of these issues, but didn't take any action until a

0:37:57.560 --> 0:38:00.920
<v Speaker 1>former Cambridge Analytica employee essentially blew the whistle on the

0:38:00.920 --> 0:38:05.080
<v Speaker 1>whole operation and it became a big public scandal. Now,

0:38:05.160 --> 0:38:08.880
<v Speaker 1>ultimately it's debatable whether any of Cambridge Analytica's efforts were

0:38:08.920 --> 0:38:12.200
<v Speaker 1>actually that effective, but the point is the company got

0:38:12.280 --> 0:38:16.719
<v Speaker 1>access to somewhere between fifty and ninety million Facebook profiles

0:38:16.719 --> 0:38:19.279
<v Speaker 1>that it should not have been able to access, and

0:38:19.320 --> 0:38:22.279
<v Speaker 1>that's a big no no. Now, both Cambridge Analytica and

0:38:22.400 --> 0:38:26.279
<v Speaker 1>Facebook would face serious repercussions for this scandal. Facebook would

0:38:26.280 --> 0:38:29.600
<v Speaker 1>face hundreds of millions of dollars in various costs, from

0:38:29.680 --> 0:38:33.840
<v Speaker 1>fines to a massive class action lawsuit settlement, and in

0:38:33.880 --> 0:38:38.359
<v Speaker 1>a separate but related matter, the Federal Trade Commission or FTC,

0:38:38.760 --> 0:38:42.960
<v Speaker 1>would find Facebook an astonishing five billion with a B

0:38:43.400 --> 0:38:47.880
<v Speaker 1>dollars for failing to practice secure and ethical data privacy policies.

0:38:48.080 --> 0:38:51.080
<v Speaker 1>Cambridge Analytica was just kind of related to this. It

0:38:51.200 --> 0:38:55.040
<v Speaker 1>was it was a specific instance of a larger problem. Now,

0:38:55.080 --> 0:38:58.719
<v Speaker 1>Cambridge Analytica would actually fold as a result of this scandal.

0:38:58.880 --> 0:39:02.359
<v Speaker 1>The company ended up essentially liquidating, but you could argue

0:39:02.440 --> 0:39:05.440
<v Speaker 1>Cambridge Analytica is not really gone because some other companies

0:39:05.440 --> 0:39:08.480
<v Speaker 1>that were related to Cambridge Analytica would continue to exist,

0:39:08.520 --> 0:39:12.280
<v Speaker 1>and they bought up the assets of Cambridge Analytica. So yeah,

0:39:12.680 --> 0:39:14.880
<v Speaker 1>you could argue it's still out there lurking, it's just

0:39:15.000 --> 0:39:18.480
<v Speaker 1>under different names. Now, the political nature of Cambridge Analytica

0:39:18.480 --> 0:39:22.120
<v Speaker 1>and the use of psychological profiling techniques really make this

0:39:22.160 --> 0:39:25.239
<v Speaker 1>particular data breach stand out. Now, you could argue there

0:39:25.280 --> 0:39:28.720
<v Speaker 1>are lots of other breaches, including ones we've already talked about,

0:39:28.760 --> 0:39:33.600
<v Speaker 1>that had a much broader scope and involved way more victims, right,

0:39:33.840 --> 0:39:38.080
<v Speaker 1>But the involvement of psychological profiling, specifically for the purposes

0:39:38.120 --> 0:39:42.800
<v Speaker 1>of affecting political campaigns makes this one seem particularly sinister.

0:39:43.160 --> 0:39:48.840
<v Speaker 1>But as I said earlier, number five actually includes Cambridge Analytica.

0:39:49.040 --> 0:39:52.600
<v Speaker 1>It's not exclusively Cambridge Analytica. That was just part of it.

0:39:52.800 --> 0:39:57.600
<v Speaker 1>The whole of number five on Chen's list is Facebook itself,

0:39:58.040 --> 0:40:01.520
<v Speaker 1>specifically with regard to an a April twenty twenty one

0:40:01.640 --> 0:40:05.759
<v Speaker 1>incident anchoring the topic, and that is where we're going

0:40:05.800 --> 0:40:08.759
<v Speaker 1>to pick up in our next episode. We'll pick up

0:40:08.800 --> 0:40:11.560
<v Speaker 1>with number five in Facebook and talk about the twenty

0:40:11.600 --> 0:40:15.360
<v Speaker 1>twenty one incident that merited entry upon this list of

0:40:15.400 --> 0:40:18.080
<v Speaker 1>the largest data breaches in US history, and then we'll

0:40:18.280 --> 0:40:20.719
<v Speaker 1>we'll you know, work our way through four, three, two

0:40:20.800 --> 0:40:23.279
<v Speaker 1>and one, and I'll probably have more to say about

0:40:23.320 --> 0:40:27.560
<v Speaker 1>ticket Master as well as we get to that. Anyway,

0:40:27.640 --> 0:40:31.400
<v Speaker 1>just as a reminder again, there's very little we as

0:40:31.640 --> 0:40:34.319
<v Speaker 1>individuals can do about these kinds of things. I mean,

0:40:34.360 --> 0:40:37.520
<v Speaker 1>if we work in the security department of these big corporations,

0:40:37.560 --> 0:40:40.439
<v Speaker 1>we can try and make sure that the practices we're

0:40:40.560 --> 0:40:45.160
<v Speaker 1>using are best practices and that we're not being laxed

0:40:45.160 --> 0:40:47.480
<v Speaker 1>at all on computer security. But for the rest of us,

0:40:47.640 --> 0:40:49.200
<v Speaker 1>you know, we can just do what we can to

0:40:49.239 --> 0:40:52.880
<v Speaker 1>protect ourselves and hope that the companies we do business

0:40:52.880 --> 0:40:55.520
<v Speaker 1>with are doing the same. And if they're not, we

0:40:55.600 --> 0:40:59.719
<v Speaker 1>can take whatever little measures we might have to mitigate

0:40:59.800 --> 0:41:02.399
<v Speaker 1>the impact it's going to have on ourselves. But really

0:41:02.440 --> 0:41:04.600
<v Speaker 1>a lot of this is out of our control. This

0:41:04.640 --> 0:41:09.120
<v Speaker 1>is why security is an everybody problem, not just on

0:41:09.160 --> 0:41:12.920
<v Speaker 1>the individual or on the company. It's everyone involved. And

0:41:13.120 --> 0:41:15.960
<v Speaker 1>it only takes one week link to make a real

0:41:16.280 --> 0:41:19.719
<v Speaker 1>entry point for malicious agents. So I know that's not

0:41:19.840 --> 0:41:22.360
<v Speaker 1>very comforting, but it's good to know the reality of

0:41:22.360 --> 0:41:25.200
<v Speaker 1>the situation that we all need to do our part

0:41:25.280 --> 0:41:27.600
<v Speaker 1>as best we can. Even that's not going to protect

0:41:27.640 --> 0:41:30.840
<v Speaker 1>us from everything, but it will at least limit the

0:41:30.960 --> 0:41:35.759
<v Speaker 1>amount of effect these hackers can have, and hopefully we'll

0:41:35.800 --> 0:41:39.000
<v Speaker 1>be able to act in such a way to minimize

0:41:39.040 --> 0:41:41.239
<v Speaker 1>the impact. If you can do that enough, then you

0:41:41.360 --> 0:41:44.319
<v Speaker 1>remove the incentive to attack in the first place. If

0:41:44.360 --> 0:41:47.760
<v Speaker 1>it's so hard to get a success in your attack,

0:41:48.320 --> 0:41:51.760
<v Speaker 1>you might figure there's a way to make money faster

0:41:52.120 --> 0:41:55.000
<v Speaker 1>and easier, some other method. So yeah, let's make it

0:41:55.080 --> 0:41:57.560
<v Speaker 1>real hard for the crooks to do crime. If we

0:41:57.640 --> 0:42:03.560
<v Speaker 1>do that, maybe they'll look something else. So that's the hope.

0:42:04.080 --> 0:42:07.320
<v Speaker 1>I hope that all of you out there are doing well,

0:42:07.600 --> 0:42:16.919
<v Speaker 1>and I will talk to you again really soon. Tech

0:42:17.000 --> 0:42:21.400
<v Speaker 1>Stuff is an iHeartRadio production. For more podcasts from iHeartRadio,

0:42:21.719 --> 0:42:25.440
<v Speaker 1>visit the iHeartRadio app, Apple Podcasts, or wherever you listen

0:42:25.440 --> 0:42:26.520
<v Speaker 1>to your favorite shows.