Difference between revisions of "Differential privacy"

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* [https://youtu.be/rfI-I3e_LFs SIGMOD 2017 Tutorial Part 1 ( 2 - 3:30pm)]
* [https://youtu.be/rfI-I3e_LFs SIGMOD 2017 Tutorial Part 1 ( 2 - 3:30pm)]
* [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)]
===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!


===Foundational Papers===
===Foundational Papers===
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* [http://www.cse.psu.edu/~sxr48/pubs/smooth-sensitivity-stoc.pdf Smooth Sensitivity]
* [http://www.cse.psu.edu/~sxr48/pubs/smooth-sensitivity-stoc.pdf Smooth Sensitivity]


===Textbook===
==Critical Papers==
 
* [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!
 
==Critical Articles to read==
===Mechanisms===
===Mechanisms===
* [http://www.cse.psu.edu/~ads22/pubs/NRS07/NRS07-full-draft-v1.pdf Smooth Sensitivity and Sampling in Private Data Analysis, 2007]
* [http://www.cse.psu.edu/~ads22/pubs/NRS07/NRS07-full-draft-v1.pdf Smooth Sensitivity and Sampling in Private Data Analysis, 2007]
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===Public Perception===
===Public Perception===
* Brooke Bullek, Stephanie Garboski, Darakhshan J. Mir, and Evan M. Peck. 2017. [https://dl.acm.org/citation.cfm?id=3025698 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
* Brooke Bullek, Stephanie Garboski, Darakhshan J. Mir, and Evan M. Peck. 2017. [https://dl.acm.org/citation.cfm?id=3025698 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===
===Philosophy===
* [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.  
* [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.  


===Existing Applications===
==Existing Applications==


;On The Map, at the US Census Bureau
===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  
* [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
===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.
* [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
===Uber===
* https://www.wired.com/story/uber-privacy-elastic-sensitivity/
* https://www.wired.com/story/uber-privacy-elastic-sensitivity/


==Advanced Topics==
===Apple===
 
* 2016-06: [https://www.wired.com/2016/06/apples-differential-privacy-collecting-data/ Andy Greenberg's article in Wired about Apple's Differential Privacy]




==Advanced Topics==




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http://repository.cmu.edu/jpc/vol7/iss3/1
http://repository.cmu.edu/jpc/vol7/iss3/1


 
=== The Fool's Gold Controversy ===
 
== 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
* 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/2016-05-19.md
* https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md
* https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md


== Other attacks ==
=== Other attacks ===
* [http://www.cse.psu.edu/~duk17/papers/definetti.pdf Attacks on Privacy and deFinetti’s Theorem], Daniel Kifer, Penn State University, 2017
* [http://www.cse.psu.edu/~duk17/papers/definetti.pdf Attacks on Privacy and deFinetti’s Theorem], Daniel Kifer, Penn State University, 2017


== Math==
=== Math===
p for randomized response rate:
p for randomized response rate:


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== See Also ==
== See Also ==
* 2016-06: [https://www.wired.com/2016/06/apples-differential-privacy-collecting-data/ Andy Greenberg's article in Wired about Apple's Differential Privacy]
* The [https://en.wikipedia.org/wiki/Differential_privacy wikipedia article on Differential Privacy] needs help. Perhaps you would like to improve it.
* The [https://en.wikipedia.org/wiki/Differential_privacy wikipedia article on Differential Privacy] needs help. Perhaps you would like to improve it.
* [[Statistical Disclosure Control]] on this wiki.
* [[Statistical Disclosure Control]] on this wiki.
* [[Secure Multiparty Computation]] on this wiki.
* [[Secure Multiparty Computation]] on this wiki.
== Online Resources ==
* [http://www.di.fc.ul.pt/~jpn/r/noise/noise.html Visualizing Noise] (in R)
* [http://www.di.fc.ul.pt/~jpn/r/noise/noise.html Visualizing Noise] (in R)

Revision as of 08:15, 10 January 2019

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

Introduction

Printed Materials

Podcasts

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.

Textbook

Foundational Papers

Critical Papers

Mechanisms

Public Perception

Philosophy

Existing Applications

On The Map, at the US Census Bureau

RAPPOR, in Google Chrome

Uber

Apple


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.

"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

Other attacks

Math

p for randomized response rate:

$p = \frac{e^\epsilon}{1+e^\epsilon}$

Probability that randomized response should be flipped.

See Also