Anonymization-Based Attacks in Privacy-Preserving Data Publishing

Data publishing generates much concern over the protection of individual privacy. Recent studies consider cases where the adversary may possess different kinds of knowledge about the data. In this article, we show that knowledge of the mechanism or algorithm of anonymization for data publication can...

Full description

Saved in:
Bibliographic Details
Published in:ACM transactions on database systems 2009-06, Vol.34 (2), p.1-46
Main Authors: Wong, Raymond Chi-Wing, Fu, Ada Wai-Chee, Wang, Ke, Pei, Jian
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Exact sciences and technology
Information systems. Data bases
Memory and file management (including protection and security)
Memory organisation. Data processing
Software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Data publishing generates much concern over the protection of individual privacy. Recent studies consider cases where the adversary may possess different kinds of knowledge about the data. In this article, we show that knowledge of the mechanism or algorithm of anonymization for data publication can also lead to extra information that assists the adversary and jeopardizes individual privacy. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. We call such an attack a minimality attack. In this article, we introduce a model called m -confidentiality which deals with minimality attacks, and propose a feasible solution. Our experiments show that minimality attacks are practical concerns on real datasets and that our algorithm can prevent such attacks with very little overhead and information loss.
ISSN:0362-5915
1557-4644
DOI:10.1145/1538909.1538910