Difference between revisions of "Differential privacy"
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A few references on Differential Privacy, for people who don't want to get bogged down with the math. | 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, [https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md Differential privacy for dummies.] (2016) | * Frank McSherry's blog post, [https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md Differential privacy for dummies.] (2016) | ||
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* [http://dimacs.rutgers.edu/~graham/pubs/slides/privdb-tutorial.pdf Building Blocks of Privacy: Differentially Private Mechanisms] (2013), Graham Cormode | * [http://dimacs.rutgers.edu/~graham/pubs/slides/privdb-tutorial.pdf Building Blocks of Privacy: Differentially Private Mechanisms] (2013), Graham Cormode | ||
=== Videos === | === Videos === | ||
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* [https://youtu.be/Uhh7QCbnE9o SIGMOD 2017 Tutorial Part 2 (4 - 5:30 pm)] | * [https://youtu.be/Uhh7QCbnE9o SIGMOD 2017 Tutorial Part 2 (4 - 5:30 pm)] | ||
==Applications== | ===Textbook=== | ||
* [https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf 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! | |||
==Deploying Differential Privacy== | |||
* [http://repository.cmu.edu/jpc/vol7/iss3/1/ 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. | |||
===Differential Privacy in Use (Applications) === | |||
;On The Map, at the US Census Bureau | |||
* [http://www.cse.psu.edu/~duk17/papers/PrivacyOnTheMap.pdf 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 | |||
* [https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42852.pdf 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/ | * https://www.wired.com/story/uber-privacy-elastic-sensitivity/ | ||
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=== Differential Privacy and Floating Point Accuracy === | === Differential Privacy and Floating Point Accuracy === | ||
Floating point math | 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 [https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html 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. | ||
* [https://www.microsoft.com/en-us/research/publication/on-significance-of-the-least-significant-bits-for-differential-privacy/ On Significance of the Least Significant Bits For Differential Privacy], Ilya Mironov, Microsoft Research, October 1, 2012. | * [https://www.microsoft.com/en-us/research/publication/on-significance-of-the-least-significant-bits-for-differential-privacy/ On Significance of the Least Significant Bits For Differential Privacy], Ilya Mironov, Microsoft Research, October 1, 2012. | ||
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http://repository.cmu.edu/jpc/vol7/iss3/1 | http://repository.cmu.edu/jpc/vol7/iss3/1 | ||
Revision as of 09:53, 15 January 2018
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
- 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)
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!
Deploying Differential Privacy
- 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.
Differential Privacy in Use (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
Advanced Topics
Improving query accuracy within the privacy budget
- 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 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)
