Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing

作     者：Abou-el-ela Abdou Hussien Nermin Hamza Hesham A. Hefny 

作者机构：Department of Computer and Information Sciences Institute of Statistical Studies and Research Cairo University Giza Egypt Department of Computer Science Faculty of Science and Humanities Shaqra University Shaqra KSA 

出 版 物：《Journal of Information Security》 (信息安全（英文）)

年 卷 期：2013年第4卷第2期

页      面：101-112页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Privacy k-Anonymity Data Mining Privacy-Preserving Data Publishing Privacy-Preserving Data Mining 

摘      要：Data mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the party running the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.