Data-driven approach for evaluating risk of disclosure and utility in differentially private data release

Kang Cheng Chen, Chia Mu Yu, Bo Chen Tai, Szu Chuang Li, Yao Tung Tsou, Yennun Huang, Chia Ming Lin

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

Differential privacy (DP) is a popular technique for protecting individual privacy and at the same for releasing data for public use. However, very few research efforts are devoted to the balance between the corresponding risk of data disclosure (RoD) and data utility. In this paper, we propose data-driven approaches for differentially private data release to evaluate RoD, and offer algorithms to evaluate whether the differentially private synthetic dataset has sufficient privacy. In addition to the privacy, the utility of the synthetic dataset is an important metric for differentially private data release. Thus, we also propose the data-driven algorithm via curve fitting to measure and predict the error of the statistical result incurred by random noise added to the original dataset. Finally, we present an algorithm for choosing appropriate privacy budget E with the balance between the privacy and utility.

原文English
主出版物標題Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
編輯Tomoya Enokido, Hui-Huang Hsu, Chi-Yi Lin, Makoto Takizawa, Leonard Barolli
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1130-1137
頁數8
ISBN(電子)9781509060283
DOIs
出版狀態Published - 5 五月 2017
事件31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 - Taipei, Taiwan
持續時間: 27 三月 201729 三月 2017

出版系列

名字Proceedings - International Conference on Advanced Information Networking and Applications, AINA
ISSN(列印)1550-445X

Conference

Conference31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
國家Taiwan
城市Taipei
期間27/03/1729/03/17

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