Full Citation

Title: Small sum privacy and large sum utility in data publishing

Citation Type: Journal Article

Publication Year: 2014

ISBN:

ISSN:

DOI: 10.1016/J.JBI.2014.04.002

NSFID:

PMCID:

PMID:

Abstract: While the study of privacy preserving data publishing has drawn a lot of interest, some recent work has shown that existing mechanisms do not limit all inferences about individuals. This paper is a positive note in response to this finding. We point out that not all inference attacks should be countered, in contrast to all existing works known to us, and based on this we propose a model called SPLU. This model protects sensitive information, by which we refer to answers for aggregate queries with small sums, while queries with large sums are answered with higher accuracy. Using SPLU, we introduce a sanitization algorithm to protect data while maintaining high data utility for queries with large sums. Empirical results show that our method behaves as desired.

Url: https://www.sciencedirect.com/science/article/pii/S1532046414000860

User Submitted?: No

Authors: Fu, Ada Wai-Chee; Wang, Ke; Wong, Raymond Chi-Wing; Wang, Jia; Jiang, Minhao

Periodical (Full): Journal of Biomedical Informatics

Issue:

Volume: 50

Pages: 20-31

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: