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)

• SIGMOD 2017 Tutorial Part 1 ( 2 - 3:30pm)
• SIGMOD 2017 Tutorial Part 2 (4 - 5:30 pm)

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

Other attacks

• Attacks on Privacy and deFinetti’s Theorem, Daniel Kifer, Penn State University, 2017

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

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.