Cornell University
Library
Cornell UniversityLibrary

eCommons

Help
Log In(current)
  1. Home
  2. Cornell Computing and Information Science
  3. Computing and Information Science
  4. Computing and Information Science Technical Reports
  5. Worst-Case Background Knowledge in Privacy

Worst-Case Background Knowledge in Privacy

File(s)
TR2006-2050.pdf (233.54 KB)
Permanent Link(s)
https://hdl.handle.net/1813/5747
Collections
Computing and Information Science Technical Reports
Author
Martin, David
Kifer, Daniel
Machanavajjhala, Ashwin
Gehrke, Johannes
Halpern, Joseph
Abstract

Recent work has shown the necessity of considering an attacker's background knowledge when reasoning about privacy in data publishing. However, in practice, the data publisher does not know what background knowledge the attacker possesses. Thus, it is important to consider the worst-case. In this paper, we initiate a formal study of worst-case background knowledge. We propose a language that can express any background knowledge about the data. We provide a polynomial time algorithm to measure the amount of disclosure of sensitive information in the worst case, given that the attacker has at most k pieces of information in this language. We also provide a method to efficiently sanitize the data so that the amount of disclosure in the worst case is less than a specified threshold.

Date Issued
2006-10-02
Publisher
Cornell University
Keywords
computer science
•
technical report
Previously Published as
http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cis/TR2006-2050
Type
technical report

Site Statistics | Help

About eCommons | Policies | Terms of use | Contact Us

copyright © 2002-2026 Cornell University Library | Privacy | Web Accessibility Assistance