A Statistical Decision-Theoretic Approach for Measuring Privacy Risk and Utility in Databases

Alisa Miyashita, Akira Kamatsuka, Takahiro Yoshida, Toshiyasu Matsushima

研究成果: Conference contribution

抄録

In this paper, we deal with the problem of database statistics publishing with privacy and utility guarantees. While various privacy and utility metrics have been proposed, purposes of using the statistics for a user and an adversary and their background knowledge about the database have not been specified. We model the user and the adversary from two perspectives. First, we model their background knowledge: knowledge of statistics of the database and knowledge of distribution for the database. Then we model the purposes of them as decision functions in statistical decision theory. Privacy and utility metrics are defined based on risk functions. Comparison of the statistical decision-theoretic framework we propose and differential privacy framework is made through a numerical example.

本文言語English
ホスト出版物のタイトル2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728140841
DOI
出版ステータスPublished - 2020 3
イベント54th Annual Conference on Information Sciences and Systems, CISS 2020 - Princeton, United States
継続期間: 2020 3 182020 3 20

出版物シリーズ

名前2020 54th Annual Conference on Information Sciences and Systems, CISS 2020

Conference

Conference54th Annual Conference on Information Sciences and Systems, CISS 2020
CountryUnited States
CityPrinceton
Period20/3/1820/3/20

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

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