Title :
Maintaining K-Anonymity against Incremental Updates
Author :
Pei, Jian ; Xu, Jian ; Wang, Zhibin ; Wang, Wei ; Wang, Ke
Author_Institution :
Simon Fraser Univ., Canda
Abstract :
K-anonymity is a simple yet practical mechanismto protect privacy against attacks of re-identifying individuals by joining multiple public data sources. All existing methods achieving k-anonymity assume implicitly that the data objects to be anonymized are given once and fixed. However, in many applications, the real world data sources are dynamic. In this paper, we investigate the problem of maintaining k-anonymity against incremental updates, and propose a simple yet effective solution. We analyze how inferences from multiple releases may temper the k-anonymity of data, and propose the monotonic incremental anonymization property. The general idea is to progressively and consistently reduce the generalization granularity as incremental updates arrive. Our new approach guarantees the k-anonymity on each release, and also on the inferred table using multiple releases. At the same time, our new approach utilizes the more and more accumulated data to reduce the information loss.
Keywords :
data privacy; database management systems; database management system; k-anonymity; monotonic incremental anonymization property; public data source; real world data source; Conference management; Data privacy; Databases; Diseases; Protection;
Conference_Titel :
Scientific and Statistical Database Management, 2007. SSBDM '07. 19th International Conference on
Conference_Location :
Banff, Alta.
Print_ISBN :
0-7695-2868-6
Electronic_ISBN :
1551-6393
DOI :
10.1109/SSDBM.2007.16