Achieve K-Anonymity with k-means Clustering and Differential Privacy
| Autor: | Hao-Wen Zheng, 鄭皓文 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 De-identification is a technique for protecting personal privacy on public dataset. It has many methods in the world. All de-identification method achieve de-identified dataset by clustering tuple on dataset, so how to cluster tuple with a better algorithm becomes a important issue. We propose a method, not only clustering algorithm of method clusters tuple on dataset is better than the known method, but also another algorithm of method improves the security of sensitive attribute, it makes the sensitive attribute of our result has better security than the known method. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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