Evaluating the risk of data disclosure using noise estimation for differential privacy

Hung Li Chen, Jia Yang Chen, Yao Tung Tsou, Chia Mu Yu, Bo Chen Tai, Szu Chuang Li, Yennun Huang, Chia Ming Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Differential privacy is a recent notion of data privacy protection, which does not matter even when an attacker has arbitrary background knowledge in advance. Consequently, it is viewed as a reliable protection mechanism for sensitive information. Differential privacy introduces Laplace noise to hide the true value in a dataset while preserving statistic properties. However, the large amount of Laplace noise added into a dataset is typically defined by the discursive scale parameter of the Laplace distribution. The privacy parameter ϵ in differential privacy is with theoretical interpretation, but the implication on the risk of data disclosure (called RoD for short) in practice has not yet been studied. Moreover, choosing appropriate value for ϵ is not an easy task since it impacts the level of privacy in a dataset significantly. In this paper, we define and evaluate the RoD in a dataset with either numerical or binary attributes for numerical or counting queries with multiple attributes based on the noise estimation. Through confidence probability of noise estimation, we give a simple way to choose the privacy parameter ϵ. Finally, we show the relation of the RoD and privacy parameter ϵ in experimental results. To the best of our knowledge, this is the first research work in using noise estimation to practically evaluate the RoD for multiple attributes (both numerical and binary data).

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 22nd Pacific Rim International Symposium on Dependable Computing, PRDC 2017
EditorsMasato Kitakami, Dong Seong Kim, Vijay Varadharajan
PublisherIEEE Computer Society
Pages339-347
Number of pages9
ISBN (Electronic)9781509056514
DOIs
StatePublished - 5 May 2017
Event22nd IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2017 - Christchurch, New Zealand
Duration: 22 Jan 201725 Jan 2017

Publication series

NameProceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC
ISSN (Print)1541-0110

Conference

Conference22nd IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2017
CountryNew Zealand
CityChristchurch
Period22/01/1725/01/17

Keywords

  • Differential privacy
  • Laplace noise
  • Multi-dimensional data
  • RoD
  • Synthetic dataset

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