Paper Info
Title
New Efficient Attacks on Statistical Disclosure Control Mechanisms
Abstract
The goal of a statistical database is to provide statistics about a population while simultaneously protecting the privacy of the individual records in the database. The tension between privacy and usability of statistical databases has attracted much attention in statistics, theoretical computer science, security, and database communities in recent years. A line of research initiated by Dinur and Nissim investigates for a particular type of queries, lower bounds on the distortion needed in order to prevent gross violations of privacy. The first result in the current paper simplifies and sharpens the Dinur and Nissim result.The Dinur-Nissim style results are strong because they demonstrate insecurity of all low-distortion privacy mechanisms. The attacks have an all-or-nothing flavor: letting ndenote the size of the database, 茂戮驴(n) queries are made before anything is learned, at which point 茂戮驴(n) secret bits are revealed. Restricting attention to a wide and realistic subset of possible low-distortion mechanisms, our second result is a more acute attack, requiring only a fixed number of queries for each bit revealed.
Year
DOI
Venue
2008
10.1007/978-3-540-85174-5_26
CRYPTO
Keywords
Field
DocType
database community,new efficient attacks,all-or-nothing flavor,dinur-nissim style result,nissim result,statistical database,possible low-distortion mechanism,statistical disclosure control mechanisms,low-distortion privacy mechanism,statistical databases,acute attack,restricting attention,lower bound
Population,Computer security,Computer science,Usability,Statistical database,Statistical disclosure control
Conference
Volume
ISSN
Citations 
5157
0302-9743
45
PageRank 
References 
Authors
4.48
19
2
Name
Order
Citations
PageRank
Cynthia Dwork19137821.87
Sergey Yekhanin298352.33