图 书 馆
在数据发布隐私与实用之间的平衡On the Tradeoff Between Privacy and Utility in Data Publishing |
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| 课程网址: | http://videolectures.net/kdd09_li_otbpud/ |
| 主讲教师: | Tiancheng Li |
| 开课单位: | 普渡大学 |
| 开课时间: | 2009-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. In the meanwhile, they reduce the utility of the data. It is important to consider the tradeoff between privacy and utility. In a paper that appeared in KDD 2008, Brickell and Shmatikov proposed an evaluation methodology by comparing privacy gain with utility gain resulted from anonymizing the data, and concluded that "even modest privacy gains require almost complete destruction of the data-mining utility". This conclusion seems to undermine existing work on data anonymization. In this paper, we analyze the fundamental characteristics of privacy and utility, and show that it is inappropriate to directly compare privacy with utility. We then observe that the privacy-utility tradeoff in data publishing is similar to the risk-return tradeoff in financial investment, and propose an integrated framework for considering privacy-utility tradeoff, borrowing concepts from the Modern Portfolio Theory for financial investment. Finally, we evaluate our methodology on the Adult dataset from the UCI machine learning repository. Our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility. |
| 关 键 词: | 计算机科学; 机器学习; 半监督学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-13：邬启凡(课程编辑志愿者) |
| 阅读次数: | 83 |