

Privacy, Anonymity, and Big Data in the Social Sciences - jcr
http://queue.acm.org/detail.cfm?id=2661641

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jcr
> _" For example, Latanya Sweeney [21] from the School of Computer Science at
> Carnegie Mellon University, has demonstrated that 87 percent of the U.S.
> population can be uniquely identified with a reasonable degree of certainty
> by a combination of ZIP code, date of birth, and gender."_

> _" [21] Sweeney, L. (2002b). Achieving k-anonymity privacy protection using
> generalization and suppression. International Journal on Uncertainty,
> Fuzziness and Knowledge-based Systems 10(5): 571-588."_

[http://dataprivacylab.org/dataprivacy/projects/kanonymity/ka...](http://dataprivacylab.org/dataprivacy/projects/kanonymity/kanonymity2.pdf)

Of course, I was told that my tin-foil hat was on far too tight when I said
you should never answer the infamous chat question, "A/S/L?"

A bit more seriously, I'm beginning to wonder it is even possible to properly
anonymize/de-identify MOOC student data well enough to satisfy all the FERPA
(Family Educational Rights and Privacy Act) legislation requirements. It seems
the "other information" requirement of the law would make compliance
impossible.

