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.


* [https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf 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!
==Introduction==


* Frank McSherry's blog post, [https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md Differential privacy for dummies.]
===Printed Materials===
* Frank McSherry's blog post, [https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md Differential privacy for dummies.] (2016)


* [https://research.neustar.biz/2014/09/08/differential-privacy-the-basics/ Introductory article by Anthony Tockar], the neustar intern who was behind the re-identificaton of the 2013 NYC taxi data release.
* [https://research.neustar.biz/2014/09/08/differential-privacy-the-basics/ Introductory article by Anthony Tockar], the neustar intern who was behind the re-identificaton of the 2013 NYC taxi data release. (2014)


* [http://dimacs.rutgers.edu/~graham/pubs/slides/privdb-tutorial.pdf Building Blocks of Privacy: Differentially Private Mechanisms], Graham Cormode
* [http://dimacs.rutgers.edu/~graham/pubs/slides/privdb-tutorial.pdf Building Blocks of Privacy: Differentially Private Mechanisms] (2013), Graham Cormode


== Video ==
 
=== Videos ===


* [https://www.youtube.com/watch?v=ekIL65D0R3o Katrina Ligett, California Institute of Technology], explains big data and differential priacy. December 17, 2013.
* [https://www.youtube.com/watch?v=ekIL65D0R3o Katrina Ligett, California Institute of Technology], explains big data and differential priacy. December 17, 2013.
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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 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 [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.
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


== Differential Privacy and the Statistical Agencies ==
* [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.





Revision as of 10:53, 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


Videos

Textbook

Deploying Differential Privacy

Differential Privacy in Use (Applications)

On The Map, at the US Census Bureau
RAPPOR, in Google Chrome
Uber

Advanced Topics

Improving query accuracy within the privacy budget

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

What's wrong with this article and with the followups?

Other attacks

Math

p for randomized response rate:

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

Probability that randomized response should be flipped.

See Also

Online Resources