Differential privacy
A few references on Differential Privacy, for people who don't want to get bogged down with the math.
- The Algorithmic Foundations of Differential Privacy, a textbook by Cynthia Dwork and Aaron Roth. The first two chapters are understable by a person who doesn't have an advanced degree in mathematics or cryptography, and it's free!
- Frank McSherry's blog post, Differential privacy for dummies.
- Introductory article by Anthony Tockar, the neustar intern who was behind the re-identificaton of the 2013 NYC taxi data release.
Video
- Katrina Ligett, California Institute of Technology, explains big data and differential priacy. December 17, 2013.
- Cynthia Dwork explains Differential Privacy, August 11, 2016. 86 minutes
- Christine Task at Purdue teachs the CERIAS Security Seminar on Differential Privacy, May 1, 2012. (40 min)
Advanced Topics
Improving query accuracy within the privacy budget
- [https://people.cs.umass.edu/~miklau/assets/pubs/dp/Li15matrix.pdf The matrix mechanism: optimizing linear counting queries
under differential privacy], Gerome Miklau, Michael Hay, Andrew McGregor, Vibhor Rastogi,The VLDB Journal, August 2015, DOI 10.1007/s00778-015-0398-x.
Differential Privacy and Floating Point Accuracy
Floating point math on computer's isn't continuous, and differential privacy implementations that assume it is may experience a variety of errors that result in privacy loss. A discussion of the problems inherently in floating-point arithmetic can be found in Oracle's What Every Computer Scientist Should Know About Floating-Point Arithmetic, an edited reprint of the paper What Every Computer Scientist Should Know About Floating-Point Arithmetic, by David Goldberg, published in the March, 1991 issue of Computing Surveys.
- On Significance of the Least Significant Bits For Differential Privacy, Ilya Mironov, Microsoft Research, October 1, 2012.
- Preserving differential privacy under finite-precision semantics, Ivan Gazeau, Dale Miller, and Catuscia Palamidessi INRIA and LIX, Ecole Polytechnique
"How Will Statistical Agencies Operate When All Data Are Private?" (MS #1142) has been published to Journal of Privacy and Confidentiality. http://repository.cmu.edu/jpc/vol7/iss3/1
Differential Privacy and the Statistical Agencies
- How Will Statistical Agencies Operate When All Data Are Private?, John M. Abowd, U.S. Census Bureau, Journal of Privacy and Confidentiality: Vol. 7 : Iss. 3 , Article 1.
The Fool's Gold Controversy
What's wrong with this article and with the followups?
- http://www.jetlaw.org/wp-content/uploads/2014/06/Bambauer_Final.pdf
- https://github.com/frankmcsherry/blog/blob/master/posts/2016-05-19.md
- https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md
Other attacks
- Attacks on Privacy and deFinetti’s Theorem, Daniel Kifer, Penn State University, 2017
Math
p for randomized response rate:
$p = \frac{e^\epsilon}{1+e^\epsilon}$
Probability that randomized response should be flipped.
See Also
- 2016-06: Andy Greenberg's article in Wired about Apple's Differential Privacy
- The wikipedia article on Differential Privacy needs help. Perhaps you would like to improve it.
- Statistical Disclosure Control on this wiki.
- Secure Multiparty Computation on this wiki.
