The Design of Access Control by using Data Dependency to Reduce the Inference of Sensitive Data

Kuei Sheng Lee, Yen Cheng Lai, Shao Yu Chen, Yi Shin Lin, Meng Feng Tsai

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

From the back-end data system point of view, the primary personal information protection mechanism is to block the direct accessing of sensitive data. The possibility that sensitive data may be indirectly inferenced by public information, have not been addressed. In United States, there are cases and discussions about "Mosaic theory". And responsibilities of data holders were legally stated. But no known researches were invested to create a responsible mechanism. This research explores the functional dependencies, and compute risky column sets based on them. We can then process users' queries and initiate protection operation if risky data are involved.

原文???core.languages.en_GB???
主出版物標題Proceedings - 25th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面68-72
頁數5
ISBN(電子)9781665403801
DOIs
出版狀態已出版 - 12月 2020
事件25th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2020 - Taipei, Taiwan
持續時間: 3 12月 20205 12月 2020

出版系列

名字Proceedings - 25th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2020

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???event.eventtypes.event.conference???25th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2020
國家/地區Taiwan
城市Taipei
期間3/12/205/12/20

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