Privacy Vulnerabilities with Background Information in
Data Perturbation

Lian Liu, Jie Wang, and Jun Zhang
Laboratory for High Performance Scientific Computing and Computer Simulation
Department of Computer Science
University of Kentucky
Lexington, KY 40506-0046, USA

Abstract

The issue of data privacy is considered a significant hindrance to the development and industrial applications of database publishing and data mining algorithms. Among many privacy-preserving methodologies, data perturbation is a popular technique for achieving the balance between data utilities and information privacy and security. It is known that the attacker's background or reference information about the original data can play a significant role in breaching data privacy. In this paper, we study the situation in which data privacy may be compromised with the leakage of a few original data records. In detail, we consider one situation in which the data owner publishes a perturbed database and the attacker knows exactly one or a few records of the original data. We find out that the remaining original data may be breached by a combination of the attacker's reference information and the perturbed data. We consider a potential privacy vulnerability with reference information in privacy-preserving database publishing and data mining based on the eigenspace of the perturbed data under some constraints. We then show that a general data perturbation model is vulnerable from this type of reference privacy breach.


Key words: Data perturbation, privacy preserving, reference information, SVD

Mathematics Subject Classification:


Download the the PDF file lianliu4.pdf.
Technical Report CMIDA-HiPSCCS 005-08, Department of Computer Science, University of Kentucky, Lexington, KY, 2008.

The research work of J. Zhang was supported in part by NSF under grants CCF-0527967 and CCF 0727600, in part by NIH under grant 1R01HL086644-01, in part by Alzheimer's Association under grant NIGR-06-25460, and in part by KSEF under grant KSEF-148-502-06-186.