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