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Volume 6 Issue 3

Semantic Security: Privacy Definitions Revisited

Jinfei Liu^(a),(*), Li Xiong^(a), Jun Luo^(b)

Transactions on Data Privacy 6:3 (2013) 185 - 198

Abstract, PDF

(a) Department of Mathematics and Computer Science; Emory University; Atlanta; USA.

(b) Huawei Noah's Ark Laboratory; HongKong and Shenzhen Institutes of Advanced Technology; Chinese Academy of Sciences; China.

e-mail:jinfei.liu @emory.edu; lxiong @mathcs.emory.edu; jun.luo @siat.ac.cn

Abstract

In this paper we illustrate a privacy framework named Indistinguishabley 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 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.

^* Corresponding author.

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