(L,K)-anonymity based on clustering
Guohua Liu · Journal of Yanshan University · 2007
K-anonymization techniques are a key component of any solution to data privacy and have been the focus of intense research in the recent years. Current solutions, however, suffer from one or more of the following limitations: reliance on pre-defined generalization hierarchies; generation of anonymized data with high information loss and the inference channel arising from lack of diversity in the sensitive information. In this paper a new privacy protection method beyond K-anonymity called (L, K)-anonym-ity and uses the idea of clustering is introduced. The algorithm of (L,K)-anonymity based on clustering is provided, the experiments shows that algorithms can eliminate the information disclosure after K-anonymity efficiently and can enforce the data security.