@inproceedings{DBLP:conf/grc/RokachS10,
  author        = {Lior Rokach and
               Alon Schclar},
  title         = {k-Anonymized Reducts},
  ee            = {http://dx.doi.org/10.1109/GrC.2010.162},
  url		= {http://www.ise.bgu.ac.il/faculty/liorr/XROKACH.pdf},
  bibsource     = {DBLP, http://dblp.uni-trier.de},
  pages         = {392-395},
  booktitle     = {2010  IEEE International Conference on Granular Computing,
               GrC 2010, San Jose, California, USA, 14-16 August 2010},
  year          = {2010},
  pages={392 -395},
  abstract={Privacy preserving data mining aims to prevent the violation of privacy that might result from mining of sensitive data. This is commonly achieved by data anonymization. One way to anonymize data is adherence to the k-anonymity concept which requires that the probability to identify an individual by linking databases not to exceed 1/k. In this paper we propose an algorithm which utilizes rough set theory to achieve k-anonymity. The basic idea is to partition the original dataset into several disjoint reducts such that each one of them adheres to k-anonymity. We show that it is easier to make each reduct comply with k-anonymity if it does not contain all quasi-identifier attributes. Moreover, our procedure ensures that even if the attacker attempts to rejoin the reducts, the k-anonymity is still preserved.},
  doi={10.1109/GrC.2010.162}, 
  keywords  = {Privacy, Anonymization, Information security}
}

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