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:209 下載:2 |
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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.
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