Name
Abstract | ||
|---|---|---|
Existing work on privacy-preserving data publishing cannot satisfactorily prevent an adversary with background knowledge from learning important sensitive information. The main challenge lies in modeling the adversary's background knowledge. We propose a novel approach to deal with such attacks. In this approach, one first mines knowledge from the data to be released and then uses the mining results as the background knowledge when anonymizing the data. The rationale of our approach is that if certain facts or background knowledge exist, they should manifest themselves in the data and we should be able to find them using data mining techniques. One intriguing aspect of our approach is that one can argue that it improves both privacy and utility at the same time, as it both protects against background knowledge attacks and better preserves the features in the data. We then present the Injector framework for data anonymization. Injector mines negative association rules from the data to be released and uses them in the anonymization process. We also develop an efficient anonymization algorithm to compute the injected tables that incorporates background knowledge. Experimental results show that Injector reduces privacy risks against background knowledge attacks while improving data utility. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICDE.2008.4497453 | ICDE |
Keywords | Field | DocType |
efficient anonymization algorithm,data privacy,injector framework,privacy-preserving data,mines knowledge,anonymization process,mining background knowledge,background knowledge mining,association rule mining,data mining,data anonymization,data mining technique,data utility,novel approach,background knowledge attack,cancer,association rules,publishing,computer science,databases,heart,data security,privacy | Data mining,Negative association,Computer science,Injector,Data anonymization,Association rule learning,Data publishing,Adversary,Information privacy,Information sensitivity,Database | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-4244-1837-4 | 35 |
PageRank | References | Authors |
1.32 | 27 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tiancheng Li | 1 | 1577 | 61.01 |
| Ninghui Li | 2 | 5863 | 305.02 |