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Controlling FD and MVD Inferences in MLS

Authors: Heping Yan; Bing Liu 0009; Xiaoming Yang; Wei Wang 0009; Baile Shi; Genxing Yang;

Controlling FD and MVD Inferences in MLS

Abstract

In today's information society, privacy protection has become a very important concern. In this paper we research the inference problems due to functional dependencies (FD) and multi-valued dependencies (MVD) in multilevel secure database (MLS) with element classification instances. To deal with the secure problem brought by inference channels we present our FD and MVD based inference control algorithms working on the finest-grained data level which greatly improve the availability of data and minimize the information loss

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
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