TY - GEN
T1 - A Statistical Decision-Theoretic Approach for Measuring Privacy Risk and Utility in Databases
AU - Miyashita, Alisa
AU - Kamatsuka, Akira
AU - Yoshida, Takahiro
AU - Matsushima, Toshiyasu
N1 - Funding Information:
This research is supported in part by Grant-in-Aid JP17K06446, JP17K00316 and JP18K11585 for Scientific Research (C), Waseda University Grant for Special Research Projects (Project number: BARE004390) and grant from Information Services International-Dentsu, Ltd, Tokyo, Japan (Project number: B2R500675001).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - 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.
AB - 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.
KW - Differential Privacy
KW - Privacy
KW - Statistical Decision Theory
KW - Utility
UR - http://www.scopus.com/inward/record.url?scp=85085262204&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085262204&partnerID=8YFLogxK
U2 - 10.1109/CISS48834.2020.1570617434
DO - 10.1109/CISS48834.2020.1570617434
M3 - Conference contribution
AN - SCOPUS:85085262204
T3 - 2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
BT - 2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th Annual Conference on Information Sciences and Systems, CISS 2020
Y2 - 18 March 2020 through 20 March 2020
ER -