Companies like Acxiom, LexisNexis, and others say there’s nothing to worry about collecting and sharing sensitive Americans’ data, as long as their names and a few other identifiers aren’t attached. After all, their reasoning is, this “anonymous” data cannot be linked to individuals and is therefore harmless.
But like me testified to the Senate last week, you can redefine anything. “Anonymous” is an abstraction. Even if a company doesn’t have your name (which they probably do), they can still get your address, internet search history, smartphone GPS logs, and other data to pin you. However, this dangerous, flawed narrative persists and continues to convince lawmakers, at the expense of strong privacy regulation.
Data on hundreds of millions of Americans’ race, gender, ethnicity, religion, sexual orientation, political beliefs, internet searches, prescriptions, and GPS location history is for sale. on the open marketand there are too many advertisers, insurance companies, loan sharks, US law enforcement agencies, scammers, and abusive individuals at home and abroad (to name a few). singular) are willing to pay for it. There is almost no regulation on data broker organization.
Many brokers argue that there is no need for regulation, because the data they buy and sell is “unlinked to individuals” simply because there is no “name” column in their spreadsheet detailing millions of mental illnesses. American god. For example, consumer credit reporting company Experian, speak extensively share data with third parties including “non-personal, unidentified or anonymous” information. Yodlee, the largest financial data broker in the US, has declare that all data it sells about Americans is “anonymous”. But companies that say such “anonymity” protects individuals from harm are untrue.
Of course, there is some difference between data that has your name (or social security number, or some other obvious identifier) attached and data that isn’t. However, the difference is small, and it keeps shrinking as the data sets get larger and larger. Think of a fun fact about yourself: If you share that spaghetti carbonara is your favorite dish to an audience of 1,000, chances are someone else in that room can say the same. The same goes for your favorite color, travel destination, or candidate in the next election. But if you had to list 50 interesting facts about yourself, the odds that all of those would apply to other people would drop dramatically. Then someone giving that list of 50 events can track that little profile back to you.
This also applies to companies with huge data sets. For example, some big data brokers like Acxiom advertise thousands or tens of thousands of individual data points on a given person. At that breadth (from sexual orientation and income levels to shopping bills and physical movement across the mall, city or country), the collective profile of each individual looks unique. . At that depth (from internet searches to 24/7 smartphone GPS logs to prescription dosages), many single data points in each person’s record can be unique as well. It’s all too easy for those organizations — and anyone who buys, licenses, or steals the data — to link all that data to specific people. Data brokers and other companies also create their own data besides a name to do it, as with mobile advertising identifier used to track people across websites and devices.
The identification becomes amazingly easy. In 2006, when AOL published a collection of 650,000 user web searches with 20 million searches, with names replaced with random numbers, The New York Times very fast link Search for specific people. (“It didn’t take long,” the reporters wrote.) Two years later, researchers at UT Austin famous Fit 500,000 Netflix users rate movies “anonymously” based on IMDb and identify users and their “obvious political preferences and other potentially sensitive information.” When the researchers examined a dataset from the New York City government, again unnamed, about every taxi ride in the city, they were not only able to soundtrack from falsely generated hash codes to identify more than 91 percent of taxis, they can also classify driver’s income.
The irony is that data brokers claiming that their “anonymous” data is risk-free is absurd: Their entire business model and marketing ploy are based on the premise that they can selectively track, understand, and micro-target each person.
