簡易檢索 / 詳目顯示

回結果列表
研究生： Leshchenko Illia
Leshchenko - Illia
論文名稱： Data Security: Modified Privacy-Preserving Data Mining Algorithm
Data Security: Modified Privacy-Preserving Data Mining Algorithm
指導教授： 洪西進
Shi-Jinn Horng
口試委員: 吳怡樂
Yi-Leh Wu
范欽雄
Chin-Shyurng Fahn
林韋宏
Lin Weihong
學位類別： 碩士
Master
系所名稱： 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 23
外文關鍵詞： Privacy-preserving data mining, k-anonymity, anonymization process
相關次數： 點閱：204  下載：2 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • Nowadays the majority of people in developed countries are using the Internet. Therefore, all of them give their personal data to third-parties, which can use it on specified conditions. However, none of the Internet websites are completely protected from malicious users, especially when those third-parties are using data mining technique, which is pretty common now. This thesis focuses on inventing a modified algorithm to provide better personal data security comparing to existing ones. This algorithm reduces a leakage of personal information for public use.
    Modified Privacy-Preserving Data Mining (MPPDM) algorithm works as follows: when data owner wants to perform data mining and publish personal information about customers, he must provide personal data anonymity first to avoid disclosure of user identity. For this purpose, he uses MPPDM and afterward can post the result for public use. The result of the algorithm is quite good and looking better than results of existing algorithms. Specification of MPPDM provides a chance to change the level of anonymity manually if it is needed. Compared to the basic PPDM, the proposed MPPDM shows better advantages.

    Abstract………………………………………………………………………………………….iii Table of contents………………………………………………………………………………..iv Chapter 1. Introduction……………………………………………………………………….1 1.1. Problem statement………………………………………………………………….1 1.2. Thesis objectives……………………………………………………………………2 1.3. Thesis organization…………………………………………………………………2 Chapter 2. Related works on PPDM and k-anonymity……………………………………...3 2.1. Privacy-preserving data mining…………………………………………………….3 2.2. k-anonymity…………………………………………………………………………4 Chapter 3. Modified privacy-preserving data mining algorithm…………………………....7 3.1. Application scenario…………………………………………………………………7 3.2. Anonymization process……………………………………………………………...8 3.3. Modifying columns algorithm of MPPDM……………………………………...….10 Chapter 4. Experimental results…………………….…………………….…………………...11 4.1. Comparing results with different level of k-anonymity……………………………..11 4.2. Advantages of MPPDM comparing to PPDM and simple k-anonymization……….13 Chapter 5. Conclusion………………………………………………………………………….14 5.1. Conclusion…………………………………………………………………………..14 5.2. Future work……..………………………………………………………………..….15 References……………………………………………………………………………………….16 Appendix………………………………………………………………………………………...17

    [1]. Data Mining: What is Data Mining? Available from: http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm
    [2]. An Introduction to Data Mining Available from: http://www.thearling.com/text/dmwhite/dmwhite.htm
    [3]. Matthew North, Data Mining for the Masses, 2012.
    [4]. Lei Xu, Chunxiao Jiang, Jian Wang, Jian Yuan, and Yong Ren, “Information Security in Big Data: Privacy and Data Mining” IEEE Access, Vol. 2, 2014, pp. 1149-1176.
    [5]. Aggarwal, Charu C., and Yu, Philip S., Privacy-Preserving Data Mining: Models and Algorithms, Springer, 2008.
    [6]. “Anonymized” data really isn’t—and here’s why not, Available from: http://arstechnica.com/tech-policy/2009/09/your-secrets-live-online-in-databases-of-ruin/
    [7]. Latanya Sweeney, “k-Anonymity: A Model for Protecting Privacy” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 10 Issue 5, Oct. 2002, pp. 557-570.
    [8]. Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, and Muthuramakrishnan Venkitasubramaniam, “l-Diversity: Privacy beyond k-Anonymity,” ACM Transactions on Knowledge Discovery from Data, Volume 1 Issue 1, March 2007.
    [9]. Policy and Law, Identifiability of De-identified Data, Available from: http://latanyasweeney.org/work/identifiability.html
    [10]. A. Hussien, N. Hamza and H. Hefny, “Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing,” Journal of Information Security, Vol. 4 No. 2, 2013, pp. 101-112.

    QR CODE