WEBVTT - Somebody's Watching All of Us

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<v Speaker 1>How do you plan to spend the holidays? Don't answer

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<v Speaker 1>the company's safe Graph probably has a good idea already.

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<v Speaker 1>I'm Jonathan Strickland and this is text Uff Daily. Adrian

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<v Speaker 1>Jeffreys wrote a piece for the Outline dot com that

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<v Speaker 1>was more than a little alarming. Jeffreys reported on a

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<v Speaker 1>study that found family members who subscribed to different political

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<v Speaker 1>ideologies spend less time together during holidays. That in itself

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<v Speaker 1>isn't alarming. What was alarming was the source of the

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<v Speaker 1>researchers data from which they drew this conclusion. That source

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<v Speaker 1>was a company called safe Graph, which gave the researchers

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<v Speaker 1>access to seventeen trillion location markers that came from ten

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<v Speaker 1>million smartphones. That means the researchers had seventeen trillion points

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<v Speaker 1>of data related to those smartphones locations over the course

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<v Speaker 1>of the sixteen Thanksgiving holiday. Even your most dedicated gum

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<v Speaker 1>shoe isn't likely to rack up that much information about

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<v Speaker 1>to mark their tailing. Using this data, the researchers were

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<v Speaker 1>able to extrapolate some other information. For example, they worked

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<v Speaker 1>under the assumption that if a smartphones location was frequently

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<v Speaker 1>in the same spot between the hours of one and

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<v Speaker 1>four am, it was most likely the home of the

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<v Speaker 1>smartphone's owner. Sure, you might go out clubbing once in

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<v Speaker 1>a while, but probably not every day of the week

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<v Speaker 1>unless you're Steffon. They also looked at location data for

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<v Speaker 1>mobile devices during the hours of one and five PM

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<v Speaker 1>on Thanksgiving Day to see how many people were spending

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<v Speaker 1>the holiday at a different location or in transit. On

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<v Speaker 1>the surface, this data is anonymous. There's no overt link

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<v Speaker 1>between the location data and any individual's identity. However, it

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<v Speaker 1>doesn't take much sleuthing to figure out which smartphone belongs

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<v Speaker 1>to any particular person. If I look at detailed location

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<v Speaker 1>markers linked to your device, and I have a passing

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<v Speaker 1>familiarity with your habits, it's probably not going to take

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<v Speaker 1>me long to connect the dots, and then I can

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<v Speaker 1>rack wherever you go. How did safe graph get this

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<v Speaker 1>information in the first place. Well, it works with app

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<v Speaker 1>developers to get that data. In some cases, safe graph

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<v Speaker 1>relies upon an API or application programming interface. The purpose

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<v Speaker 1>of an API is to allow developers the chance to

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<v Speaker 1>create new apps that can communicate with and take advantage

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<v Speaker 1>of other apps. This allows for a mutually beneficial relationship

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<v Speaker 1>between multiple applications. In other cases, safe graph negotiates a

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<v Speaker 1>price for that information. It's true everywhere, but especially for

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<v Speaker 1>the online world, information is valuable. There are entire companies

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<v Speaker 1>that exist solely to buy and sell massive amounts of

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<v Speaker 1>data about users. And again, while on the surface that

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<v Speaker 1>data may not be associated with a specific name, it

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<v Speaker 1>typically doesn't take a lot of work to associate a

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<v Speaker 1>specific person with a particular device or set of behaviors.

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<v Speaker 1>Studies bear this out. A research paper published in Science

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<v Speaker 1>in showed that by analyzing meta data connected to credit

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<v Speaker 1>card transactions, it was trivial to connect purchases to specific people.

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<v Speaker 1>This was after all personal data had been scrubbed from

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<v Speaker 1>the information. The data sets included dates, the amounts that

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<v Speaker 1>were charged, and the stores that were visited. There were

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<v Speaker 1>no names, no credit card numbers, or any other information

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<v Speaker 1>that overtly identified the card owners. It was the behavior

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<v Speaker 1>of shoppers that made them easy to identify. That, coupled

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<v Speaker 1>with publicly available information such as public posts on social media,

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<v Speaker 1>made it simple to re identify nine of the shoppers

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<v Speaker 1>in the study. This research paper isn't an outlier. There

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<v Speaker 1>have been numerous other studies that have shown that massive

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<v Speaker 1>amounts of supposedly anonymous information give anyone with patients enough

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<v Speaker 1>of a lead to identify specific people among that data set,

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<v Speaker 1>and as you might imagine, this poses a serious threat

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<v Speaker 1>to people's privacy. It doesn't take a worse case scenario

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<v Speaker 1>to see how all this data mining and analysis could

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<v Speaker 1>go wrong. There are plenty of people who would love

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<v Speaker 1>to take advantage of all that information. Some want to

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<v Speaker 1>send targeted advertising to users. In a typical implementation, an

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<v Speaker 1>algorithm would match adds to the people most likely to

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<v Speaker 1>find those ads interesting. Some people might find that approach

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<v Speaker 1>off putting or irritating, but it's one of the more

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<v Speaker 1>benign uses for all that data. Others might take the

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<v Speaker 1>information and use it for all sorts of nefarious purposes.

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<v Speaker 1>It's not a stretch to imagine a situation in which

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<v Speaker 1>someone uses this kind of data to blackmail a target.

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<v Speaker 1>Just imagine getting a phone call and hearing someone say, hey,

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<v Speaker 1>you called out sick to work yesterday, but I see

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<v Speaker 1>you were actually visiting an amusement park all day long.

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<v Speaker 1>Not that I would ever miss work to go to

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<v Speaker 1>an amusement park. Or imagine an insurance company combing through

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<v Speaker 1>this data to figure out how much it should charge

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<v Speaker 1>customers based on their behaviors, or a company using it

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<v Speaker 1>to keep an eye on what employees are doing off

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<v Speaker 1>the clock. It's a massive invasion of privacy. So what's

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<v Speaker 1>the solution? From a personal standpoint? You can turn off

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<v Speaker 1>location features on your devices That will help a bit.

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<v Speaker 1>You can avoid attaching location data to social posts. You

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<v Speaker 1>can turn social posts to private or limit giving information

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<v Speaker 1>about the places you go to and the things you

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<v Speaker 1>do on social media. In short, you can withdraw from

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<v Speaker 1>many of the activities that are becoming more common in

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<v Speaker 1>everyday life. It's not a fun answer, but it is

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<v Speaker 1>an honest one. Even then, there are things that may

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<v Speaker 1>be outside your awareness or control that are being tracked. Ultimately,

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<v Speaker 1>what we might need our regulations on how companies can

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<v Speaker 1>collect and perhaps more importantly, profit from the data we generate.

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<v Speaker 1>Here's hoping you have a happy and safe holiday season.

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<v Speaker 1>If you want to learn more about online privacy, GPS,

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<v Speaker 1>technology and big data, subscribe to The Tech Stuff podcast.

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<v Speaker 1>We explore technology on all scales in a long form

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<v Speaker 1>podcast that publishes every Wednesday and Friday. I'll see you

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<v Speaker 1>again soon.