k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous data, the model has been that of a single source wanting to publish data. This paper presents a k-anonymity protocol when the data is vertically partitioned between sites. A key contribution is a proof that the protocol preserves k-anonymity between the sites: While one site may have individually identifiable data, it learns nothing that violates k-anonymity with respect to the data at the other site. This is a fundamentally different distributed privacy definition than that of Secure Multiparty Computation, and it provides a better match with both ethical and legal views of privacy. © IFIP International Federation for Information Processing 2005.
CITATION STYLE
Jiang, W., & Clifton, C. (2005). Privacy-preserving distributed k-anonymity. In Lecture Notes in Computer Science (Vol. 3654, pp. 166–177). Springer Verlag. https://doi.org/10.1007/11535706_13
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