大数据外文中英文翻译参考文献综述.doc

大数据外文翻译参考文献综述 (文档含中英文对照即英文原文和中文翻译) 原文： Data Mining and Data Publishing Data mining is the extraction of vast interesting patterns orknowledge from huge amount of data. The initial idea ofprivacy-preserving data mining PPDM was to extend traditional datamining techniques to work with the data modified to mask sensitiveinformation. The key issues were how to modify the data and how torecover the data mining result from the modified data. Privacy-preservingdata mining considers the problem of running data mining algorithms onconfidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing(PPDP) may not necessarily be tied to a specific data mining task, and thedata mining task may be unknown at the time of data publishing. PPDPstudies how to transform raw data into a version that is immunizedagainst 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 ofprivacy sensitive data for analysis purposes. One well studied approach isthe k-anonymity model [1] which in turn led to other models such asconfidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. Inparticular, all known mechanisms try to minimize information loss andsuch an attempt provides a loophole for attacks. The aim of this paper isto present a survey for most of the common attacks techniques foranonymization-based PPDM PPDP and explain their effects on DataPrivacy. Although data mining is potentially useful, many data holders arereluctant to provide their data for data mining for the fear of violatingindividual privacy. In recent years, study has been made to ensure that thesensitive information of individuals cannot be identified easily. Anonymity Models, k-anonymization techniques have been thefocus of intense research in the last few years. In order to ensureanonymization of