Injector: Mining Background Knowledge for Data Anonymization
Author
Tiancheng Li; Ninghui Li
Tech report number
CERIAS TR 2008-29
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.
Institution
Purdue University
Journal
International Conference on Data Engineering (ICDE), 2008
Publisher
IEEE Computer Society
Affiliation
Center for Education and Research Information Assurance and Security
Publication Date
2008-04-07
Keywords
Data Privacy and Security; Anonymization; Data Mining