Differential privacy

A few references on Differential Privacy, for people who don't want to get bogged down with the math.

Introduction

Printed Materials

• Frank McSherry's blog post, Differential privacy for dummies. (2016)

• Introductory article by Anthony Tockar, the neustar intern who was behind the re-identificaton of the 2013 NYC taxi data release. (2014)

• Building Blocks of Privacy: Differentially Private Mechanisms (2013), Graham Cormode

Videos

• Four Facets of Differential Privacy, Differential Privacy Symposium, Institute for Advanced Study, Princeton, Saturday, November 12. A series of talks by Cynthia Dwork, Helen Nissenbaum, Aaron Roth, Guy Rothblum, Kunal Talwar, and Jonathan Ullman. View all on the IAS YouTube channel.

• 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)

Textbook

• The Algorithmic Foundations of Differential Privacy (2014), 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!

Critical Articles to read

Mechanisms

• Smooth Sensitivity and Sampling in Private Data Analysis, 2007
• Differential Privacy for Statistics: What we Know and What we Want to Learn, 2009
• Towards Practical Differential Privacy for SQL Queries, 2017
• 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.

Public Perception

• Brooke Bullek, Stephanie Garboski, Darakhshan J. Mir, and Evan M. Peck. 2017. Towards Understanding Differential Privacy: When Do People Trust Randomized Response Technique?. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 3833-3837. DOI: https://doi.org/10.1145/3025453.3025698

Philosophy

• 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.

Existing Applications

On The Map, at the US Census Bureau

• Privacy: Theory meets Practice on the Map, Machanavajjhala, Kifer, Abowd, Gehrke, and Vilhuber, ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, Pages 277-286

RAPPOR, in Google Chrome

• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response, Erlingsson, PIhur, and Korolova, CCS’14, November 3–7, 2014, Scottsdale, Arizona, USA.

Uber

• https://www.wired.com/story/uber-privacy-elastic-sensitivity/

Advanced Topics

Differential Privacy and Floating Point Accuracy

Floating point math is not 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

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.

Online Resources

• Visualizing Noise (in R)