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Encyclopedia of Cryptography and Security pp 338–340Cite as

Differential Privacy

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Synonyms

ε-Differential privacy; ε-Indistinguishability

Related Concepts

Data Confidentiality; Informed Adversary; Macrodata Protection; Microdata Protection; Private Data Analysis; Statistical Disclosure Control

Definition

Differential privacy is a meaningful and mathematically rigorous definition of privacy useful for quantifying and bounding privacy loss. Developed in the context of statistical disclosure control – providing accurate statistical information about a set of respondents while protecting the privacy of each individual – the concept applies more generally to any private data set for which it is desirable to release coarse-grained information while keeping private the details. Informally, differential privacy requires the probability distribution on the published results of an analysis to be “essentially the same,” independent of whether any individual opts in to or opts out of the data set. The probabilities are over the coin flips of the data analysis algorithm.

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Recommended Reading

  1. Chaudhuri K, Monteleoni C (2008) Privacy-preserving logistic regression. In: Proceedings of advances in neural information processing systems (NIPS), Vancouver

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  2. Dinur I, Nissim K (2003) Revealing information while preserving privacy. In: Proceedings of the 22nd ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems, San Jose, pp 202–210

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  3. Dwork C (2006) Differential privacy. In: Proceedings of the 33rd International colloquium on automata, languages and programming (ICALP)(2), Venice, pp 1–12

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  4. Dwork C, Kenthapadi K, McSherry F, Mironov I, Naor M (2006) Our data, ourselves: privacy via distributed noise generation. In: Advances in cryptology: proceedings of EUROCRYPT, Springer, New York, pp 486–503

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  5. Dwork C, McSherry F, Nissim K, Smith A (2006) Calibrating noise to sensitivity in private data analysis. In: Proceedings of the 3rd theory of cryptography conference, New York, NY, pp 265–284

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  6. Dwork C, Nissim K (2004) Privacy-preserving datamining on vertically partitioned databases. In: Proceedings of CRYPTO 2004, vol 3152. LNCS, Springer, Heidelberg, pp 528–544

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  7. Dwork C (2010) A firm foundation for private data analysis. Commun ACM (To appear) (available at http://research.microsoft.com/en-us/projects/databaseprivacy)

  8. McSherry F (2009) Privacy integrated queries. In: Proceedings of the 2009 ACM SIGMOD international conference on management of data (SIGMOD), Providence

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  9. McSherry F, Talwar K (2007) Mechanism design via differential privacy. In: Proceedings of the 48th annual symposium on foundations of computer science, Providence

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  10. Roy I, Setty S, Kilzer A, Shmatikov V, Witchel E (2010) Airavat: security and privacy for MAPREDUCE. In: Proceedings of the 7th USENIX symposium on networked systems design and implementation (NSDI), Cambridge

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Author information

Authors and Affiliations

  1. Microsoft Research, Silicon Valley, 1065 La Avenida, 94043, Mountain View, CA, USA

    Cynthia Dwork

Authors
  1. Cynthia Dwork
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computing Science, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands

    Henk C. A. van Tilborg

  2. Center for Secure Information Systems, George Mason University, Fairfax, VA, 22030-4422, USA

    Sushil Jajodia

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© 2011 Springer Science+Business Media, LLC

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Dwork, C. (2011). Differential Privacy. In: van Tilborg, H.C.A., Jajodia, S. (eds) Encyclopedia of Cryptography and Security. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5906-5_752

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