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- W3021695936 abstract "These days, huge data size requires high-end resources to be stored in IT organizations premises. They depend on cloud for additional resource necessities. Since cloud is a third-party, we cannot guarantee high security for our information as it might be misused. This necessitates the need of privacy in data before sharing to the cloud. Numerous specialists proposed several methods, wherein they attempt to discover explicit identifiers and sensitive data before distributing it. But, quasi-identifiers are attributes which can spill data of explicit identifiers utilizing background knowledge. Analysts proposed strategies to find quasi- identifiers with the goal that these properties can likewise be considered for implementing privacy. But, these techniques suffer from many drawbacks like higher time consumption and extract more quasi identifiers which decreases data utility. The proposed work overcomes this drawback by extracting minimum required quasi attributes with minimum time complexity." @default.
- W3021695936 created "2020-05-13" @default.
- W3021695936 creator A5087045475 @default.
- W3021695936 date "2020-04-26" @default.
- W3021695936 modified "2023-09-26" @default.
- W3021695936 title "Articulation Point Based Quasi Identifier Detection for Privacy Preserving in Distributed Environment" @default.
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- W3021695936 doi "https://doi.org/10.54039/ijcnis.v12i1.4426" @default.
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