期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2009
卷号：3
期号：1
出版社：SERSC
摘要：Many efforts have been done in the field of privacy preservation to devise algorithms for data k-anonymization and l-diversification trying to protect privacy, by modification of data, for example. Fewer efforts have been made for devising techniques, tools and methodologies for investigation and evaluation of privacy risks. We are concerned about privacy diagnosis before starting protection. Actually we show privacy leakages threaten data publication. We introduce a Privacy Diagnosis Centre for this purpose. In this paper toward this diagnosis centre we focus on anonymity and, in particular, k-anonymity. Then we aim at k-anonymity diagnosis system. Such a system explores various questions about k-anonymity of data. “For which k is my data k-anonymous?”, “is my data sufficiently k-anonymous?”, “which subset and projection of data can be safely published to guarantee given k?”, “which information, if available from an outside source, threatens the k-anonymity of my data?” are examples of questions can be answered. We leverage two properties of k-anonymity that we express in the form of two lemmas. The first lemma is a monotonicity property that enables us to adapt the a-priori algorithm for k-anonymity. The second lemma, however, is a determinism property that enables us to devise an efficient algorithm for δ-suppression. We illustrate and empirically analyze the performance of the proposed algorithms.
