Difference between revisions of "Differential privacy"
m |
m |
||
| Line 26: | Line 26: | ||
== 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== | |||
p for randomized response rate: | |||
$p = \frac{e^\epsilon}{1+e^\epsilon}$ | |||
Probability that randomized response should be flipped. | |||
== See Also == | == See Also == | ||
Revision as of 12:43, 20 February 2017
A few references on Differential Privacy, for people who don't want to get bogged down with the math.
- 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!
- Frank McSherry's blog post, Differential privacy for dummies.
- Introductory article by Anthony Tockar, the neustar intern who was behind the re-identificaton of the 2013 NYC taxi data release.
Video
- 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)
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 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
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.
