Big Data is a term used to describe all the bits of information collected by companies and the government about you.
It’s used primarily by marketers to deliver ads to you as you check your Facebook page , read the latest online news and shop for a new pair of shoes.
However, that same data could be the reason you didn’t get a job, were denied health insurance or failed to qualify for a second mortgage. And that’s one reason why the Federal Trade Commission may step in.
In a speech this week, FTC commissioner Julie Brill proposed “Reclaim Your Name,” a program that would force Big Data brokers, such as Axciom, to be treated the same way as credit reporting companies are under the Fair Credit Reporting Act.
Under Brill’s plan, companies who collect data and those who sell data to third parties would have to provide an easy way for people to find out what data has been collected, correct any errors and decide whether the company can sell the data.
In other words, you, not a faceless corporation, would determine the fate of your data.
The biggest data broker on the planet, Arkansas-based Axciom, will try to beat the FTC to the punch. The company claims to have information on nearly 1 billion online users, including 90 percent of all U.S. social profiles, CEO Scott Howe said, as reported by Forbes.
It plans to give consumers access to a “pretty healthy amount” of data, a program that was scheduled to be available by midsummer, but has been delayed. [See also: How Big Data Organizing Netted $15 Million ]
But Axciom’s plan won’t go far enough for the FTC. Despite claims by data collectors and brokers that the information they use as been anonymized, meaning names, addresses and other personally identifiable information has been removed, Brill said that’s not enough.
“Every day we hear how easy it is to reattach identity to data that has been supposedly scrubbed,” Brill said. “NSF-funded research by Alessandro Acquisti has shown that, using publicly available online data and off-the-shelf facial recognition technology, it is possible to predict — with an alarming level of accuracy — identifying information as private as an individual’s Social Security number from an anonymous snapshot.”
(Brill herself may not be aware of the extremely limited conditions of Acquisti’s experiment, which matched on-campus snapshots to headshots in the Carnegie Mellon student directory. Acquisti then used student biographical data to predict Social Security numbers, which are generally numbered according to region and date.)
Brill wants companies to ensure this doesn’t happen.
“They should do everything technically possible to strip their data of identifying markers; they should make a public commitment not to try to re-identify the data; and they should contractually prohibit downstream recipients from doing the same,” she said.
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