To Reduce the Inference of Sensitive Data, Design Access Control by Data Dependency

• Main Authors: Yen-Cheng Lai, 賴彥丞
• Other Authors: Meng-Feng Tsai
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/qxdx4g

碩士 === 國立中央大學 === 資訊工程學系 === 107 === From the back-end data system point of view, the primary personal information protection mechanism is to block the direct accessing of sensitive data. We have observed the related issues in fields of Institutional Research, as well as governments’ information publication. And 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 may lead to a situation where data holders will not willingly integrate, exchange, and publish their data. Our society may not be able to comprehensively understand ourselves and conduct effective analysis, even though we do have huge volume oh data. 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.