Data and Goliath by Bruce Schneier Read Free Book Online

camera will get even less avoidable
     as life recorders become more prevalent. Once enough people regularly record video
     of what they are seeing, you’ll be in enough of their video footage that it’ll no
     longer matter whether or not you’re wearing one. It’s kind of like herd immunity,
     but in reverse.
    UBIQUITOUS SURVEILLANCE
    Philosopher Jeremy Bentham conceived of his “panopticon” in the late 1700s as a way
     to build cheaper prisons. His idea was a prison where every inmate could be surveilled
     at any time, unawares. The inmate would have no choice but to assume that he was always
     being watched, and would therefore conform. This idea has been used as a metaphor
     for mass personal data collection, both on the Internet and off.
    On the Internet, surveillance is ubiquitous. All of us are being watched, all the
     time, and that data is being stored forever. This is what an information-age surveillance
     state looks like, and it’s efficient beyond Bentham’s wildest dreams.

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    Analyzing Our Data
    I n 2012, the New York Times published a story on how corporations analyze our data for advertising advantages.
     The article revealed that Target Corporation could determine from a woman’s buying
     patterns that she was pregnant, and would use that information to send the woman ads
     and coupons for baby-related items. The story included an anecdote about a Minneapolis
     man who’d complained to a Target store that had sent baby-related coupons to his teenage
     daughter, only to find out later that Target was right.
    The general practice of amassing and saving all kinds of data is called “big data,”
     and the science and engineering of extracting useful information from it is called
     “data mining.” Companies like Target mine data to focus their advertising. Barack
     Obama mined data extensively in his 2008 and 2012 presidential campaigns for the same
     purpose. Auto companies mine the data from your car to design better cars; municipalities
     mine data from roadside sensors to understand driving conditions. Our genetic data
     is mined for all sorts of medical research. Companies like Facebook and Twitter mine
     our data for advertising purposes, and have allowed academics to mine their data for
     social research.
    Most of these are secondary uses of the data. That is, they are not the reason thedata was collected in the first place. In fact, that’s the basic promise of big data:
     save everything you can, and someday you’ll be able to figure out some use for it
     all.
    Big data sets derive value, in part, from the inferences that can be made from them.
     Some of these are obvious. If you have someone’s detailed location data over the course
     of a year, you can infer what his favorite restaurants are. If you have the list of
     people he calls and e-mails, you can infer who his friends are. If you have the list
     of Internet sites he visits—or maybe a list of books he’s purchased—you can infer
     his interests.
    Some inferences are more subtle. A list of someone’s grocery purchases might imply
     her ethnicity. Or her age and gender, and possibly religion. Or her medical history
     and drinking habits. Marketers are constantly looking for patterns that indicate someone
     is about to do something expensive, like get married, go on vacation, buy a home,
     have a child, and so on. Police in various countries use these patterns as evidence,
     either in a court or in secret. Facebook can predict race, personality, sexual orientation,
     political ideology, relationship status, and drug use on the basis of Like clicks
     alone. The company knows you’re engaged before you announce it, and gay before you
     come out—and its postings may reveal that to other people without your knowledge or
     permission. Depending on the country you live in, that could merely be a major personal
     embarrassment—or it could get you killed.
    There are a lot of errors in these inferences, as all of us who’ve seen