Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICDE.2008.4497481 | ICDE |
Keywords | Field | DocType |
privacy threat,anonymized data publication,alternative anonymization technique,paper deal,strong privacy guarantee,new type,proposed technique,conventional generalization,anti-corruption privacy preserving publication,severe privacy disclosure,games,tin,privacy,stratified sampling,data privacy,information science,perturbation,integrated circuits,data mining,computer science,data engineering,sampling methods | Data mining,Internet privacy,Computer security,Computer science,Stratified sampling,Adversary,Information privacy,Corruption,Database | Conference |
ISSN | Citations | PageRank |
1084-4627 | 34 | 1.29 |
References | Authors | |
27 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yufei Tao | 1 | 7231 | 316.71 |
| Xiaokui Xiao | 2 | 3266 | 142.32 |
| Jiexing Li | 3 | 211 | 10.36 |
| Donghui Zhang | 4 | 228 | 9.71 |