比较近似和概率微分隐私参数

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing Letters Pub Date : 2023-08-01 DOI:10.1016/j.ipl.2023.106380

Vincent Guingona , Alexei Kolesnikov , Julianne Nierwinski , Avery Schweitzer

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引用次数: 1

摘要

本文比较了差分隐私的两个概念：近似差分隐私和概率差分隐私。众所周知，PrDP意味着ADP；并且在[7]中确定，在对两种性质的定义中使用的参数ε和δ进行惩罚之后，ADP意味着PrDP。我们证明了[7]中发现的条件是严格的：如果失败，我们构建一个具有ADP但没有PrDP的随机算法。

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Comparing approximate and probabilistic differential privacy parameters

This paper compares two notions of differential privacy: approximate differential privacy (ADP) and probabilistic differential privacy (PrDP). It is well-known that the PrDP implies the ADP; and it was established in [7] that the ADP implies the PrDP, after a penalty on the parameters ε and δ that are used in the definitions of both properties. We show that the condition found in [7] is tight: if it fails, we construct a randomized algorithm that has ADP but not PrDP.

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来源期刊

Information Processing Letters 工程技术-计算机：信息系统

CiteScore

1.80

自引率

0.00%

发文量

审稿时长

7.3 months

期刊介绍： Information Processing Letters invites submission of original research articles that focus on fundamental aspects of information processing and computing. This naturally includes work in the broadly understood field of theoretical computer science; although papers in all areas of scientific inquiry will be given consideration, provided that they describe research contributions credibly motivated by applications to computing and involve rigorous methodology. High quality experimental papers that address topics of sufficiently broad interest may also be considered. Since its inception in 1971, Information Processing Letters has served as a forum for timely dissemination of short, concise and focused research contributions. Continuing with this tradition, and to expedite the reviewing process, manuscripts are generally limited in length to nine pages when they appear in print.