Difference between revisions of "Differential privacy"

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


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


=== Podcasts ===
=== Podcasts ===

Revision as of 13:05, 19 August 2018

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

Critical Articles to read

Mechanisms

Public Perception


Philosophy

Existing Applications

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

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

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