Closeness: A New Privacy Measure for Data Publishing

  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},
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
Highly Cited
This paper has 159 citations. REVIEW CITATIONS

14 Figures & Tables



Citations per Year

160 Citations

Semantic Scholar estimates that this publication has 160 citations based on the available data.

See our FAQ for additional information.