Abstract
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.
This material is based upon work supported by the National Science Foundation under Grant No. 0428168.
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Jiang, W., Clifton, C. (2005). Privacy-Preserving Distributed k-Anonymity. In: Jajodia, S., Wijesekera, D. (eds) Data and Applications Security XIX. DBSec 2005. Lecture Notes in Computer Science, vol 3654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11535706_13
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DOI: https://doi.org/10.1007/11535706_13
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