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A privacy framework: indistinguishable privacy

Published: 18 March 2013 Publication History

Abstract

In this paper we illustrate a privacy framework named Indistinguishable Privacy. Indistinguishable privacy could be deemed as the formalization of the existing privacy definitions in privacy preserving data publishing as well as secure multi-party computation. We introduce three variants of the representative privacy notions in the literature, Bayes-optimal privacy for privacy preserving data publishing, differential privacy for statistical data release, and privacy w.r.t. semi-honest behavior in the secure multi-party computation setting, and prove they are equivalent. To the best of our knowledge, this is the first work that illustrates the relationships of these privacy definitions and unifies them through one framework.

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cover image ACM Other conferences
EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops
March 2013
423 pages
ISBN:9781450315999
DOI:10.1145/2457317
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 March 2013

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Author Tags

  1. k-anonymity
  2. Bayes-optimal privacy
  3. differential privacy
  4. indistinguishable privacy
  5. privacy w.r.t. semi-honest behavior
  6. secure multi-party computation

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EDBT/ICDT '13

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EDBT '13 Paper Acceptance Rate 7 of 10 submissions, 70%;
Overall Acceptance Rate 7 of 10 submissions, 70%

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