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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 25th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages68-72
Number of pages5
ISBN (Electronic)9781665403801
DOIs
StatePublished - Dec 2020
Event25th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2020 - Taipei, Taiwan
Duration: 3 Dec 20205 Dec 2020

Publication series

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

Conference

Conference25th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2020
Country/TerritoryTaiwan
CityTaipei
Period3/12/205/12/20

Keywords

  • data inference
  • functional dependency
  • information system security control
  • mosaic theory
  • personal information protection

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