Closeness: A New Privacy Measure for Data Publishing

@article{Li2010ClosenessAN,
  title={Closeness: A New Privacy Measure for Data Publishing},
  author={Ninghui Li and Tiancheng Li and Suresh Venkatasubramanian},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2010},
  volume={22},
  pages={943-956}
}
The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain “identifying” attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of ℓ-diversity has been proposed to address this; ℓ-diversity requires that each equivalence class has at least ℓ well-represented (in Section 2… CONTINUE READING
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