大数据外文翻译参考文献综述文档格式.docx

1、大数据外文翻译参考文献综述#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#Datamining is theextractionof vastinteresting patternsor knowledgefromhugeamountofdata.Theinitialideaof privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modi

2、fied 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

3、 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 pri

4、vacy 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

5、which in turn led to other models such as confidence bounding, l-diversity, t-closeness, （,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

6、attacks techniques for anonymization-based PPDM &# PPDP and explain their effects on Data Privacy.#Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to e

7、nsure that the sensitive information of individuals cannot be identified easily.#Anonymity Models, k-anonymization techniques have been the#focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the information#loss resulting from

8、data modifications, everal extending models are proposed, which are discussed as follows.#1.k-Anonymity#k-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniq

9、uely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous （k 1） if each record in the data set is in- distinguishable from at least （k . 1） other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can en

10、sure that individuals cannot be uniquely identified by linking attacks.#2. Extending Models#Since k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problemby requiringthateachequivalenceclasshasatleastl well-represented

11、value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l wel

12、l-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and differentvalueshaveverydifferentlevelsofsensitivity.After#anonymization, in any equivalence class, the frequency （in fraction） of a sensitive value is no more than .#3. Related R

13、esearch Areas#Several polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy in

14、trusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness （TIA）, was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyz

15、ing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining （PPDM） emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive informat

16、ion. The key issues were how to modify the data and how to recover thedata mining result from the modified data.#The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing （PPDP） may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the tim