On Anti-Corruption Privacy Preserving Publication

Yufei Tao, Xiaokui Xiao, Jiexing Li, and Donghui Zhang

In International Conference on Data Engineering (ICDE), 2008

This paper deals with a new type of privacy threat, called corruption, in anonymized data publication. Specifically, an adversary is said to have corrupted some individuals, if s/he has already obtained their sensitive values before consulting the released information. Conventional generalization may lead to severe privacy disclosure in the presence of corruption. Motivated
by this, we advocate an alternative anonymization technique that integrates generalization with perturbation and stratified sampling. The integration provides strong privacy guarantees, even if an adversary has corrupted any number of individuals. We verify the effectiveness of the proposed technique through experiments with real data.