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International Conference on Information Security

ISC 2011: Information Security pp 325–340Cite as

How Much Is Enough? Choosing ε for Differential Privacy

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Part of the Lecture Notes in Computer Science book series (LNSC,volume 7001)

Abstract

Differential privacy is a recent notion, and while it is nice conceptually it has been difficult to apply in practice. The parameters of differential privacy have an intuitive theoretical interpretation, but the implications and impacts on the risk of disclosure in practice have not yet been studied, and choosing appropriate values for them is non-trivial. Although the privacy parameter ε in differential privacy is used to quantify the privacy risk posed by releasing statistics computed on sensitive data, ε is not an absolute measure of privacy but rather a relative measure. In effect, even for the same value of ε, the privacy guarantees enforced by differential privacy are different based on the domain of attribute in question and the query supported. We consider the probability of identifying any particular individual as being in the database, and demonstrate the challenge of setting the proper value of ε given the goal of protecting individuals in the database with some fixed probability.

Keywords

  • Differential Privacy
  • Privacy Parameter
  • ε

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Author information

Authors and Affiliations

  1. Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA

    Jaewoo Lee & Chris Clifton

Authors
  1. Jaewoo Lee
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  2. Chris Clifton
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Min Hang Shanghai, China

    Xuejia Lai

  2. Cryptography and Security Department, Institute for Infocomm Research, Singapore

    Jianying Zhou

  3. Key Laboratory of Computer Networks and Information, Security Xidian University, 2 South Taibai Road, Xi’an, 710071, Shaanxi, China

    Hui Li

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Lee, J., Clifton, C. (2011). How Much Is Enough? Choosing ε for Differential Privacy. In: Lai, X., Zhou, J., Li, H. (eds) Information Security. ISC 2011. Lecture Notes in Computer Science, vol 7001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24861-0_22

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  • DOI: https://doi.org/10.1007/978-3-642-24861-0_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24860-3

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