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      Multirelational k-anonymity

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      Author(s)
      Nergiz, M.E.
      Clifton, C.
      Nergiz, A.E.
      Date
      2009
      Source Title
      IEEE Transactions on Knowledge and Data Engineering
      Print ISSN
      10414347
      Volume
      21
      Issue
      8
      Pages
      1104 - 1117
      Language
      English
      Type
      Article
      Item Usage Stats
      159
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      122
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      Abstract
      k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous data set, any identifying information occurs in at least k tuples. Much research has been done to modify a single-table data set 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. We also propose two new clustering algorithms to achieve multirelational anonymity. Experiments show the effectiveness of the approach in terms of utility and efficiency. © 2006 IEEE.
      Keywords
      Integrity
      Privacy
      Protection
      Relational database
      Security
      Integrity
      Privacy
      Protection
      Relational database
      Security
      Clustering algorithms
      Permalink
      http://hdl.handle.net/11693/22677
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TKDE.2008.210
      Collections
      • Computer Technology and Information Systems 62
      • Department of Computer Engineering 1435
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