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      Multirelational k-anonymity

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      Author(s)
      Nergiz, M. Ercan
      Clifton, C.
      Nergiz, A. Erhan
      Date
      2007-04
      Source Title
      Proceedings - International Conference on Data Engineering
      Publisher
      IEEE
      Pages
      1417 - 1421
      Language
      English
      Type
      Conference Paper
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      Abstract
      k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much research has been done to modify a single table dataset to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy, or overly reduce the utility of the data, in a multiple relation setting. A new clustering algorithm is proposed to achieve multirelational anonymity. © 2007 IEEE.
      Keywords
      Clustering algorithms
      Constraint theory
      Data structures
      Database systems
      Anonymity constraints
      Multirelational anonymity
      Data privacy
      Permalink
      http://hdl.handle.net/11693/27095
      Published Version (Please cite this version)
      https://doi.org/10.1109/ICDE.2007.369025
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      • Department of Computer Engineering 1435
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