Abstract
We study the problem of publishing a data table containing personal information, while ensuring individual privacy and maintaining data integrity to the possible extent. One popular technique in literature is through k-anonymization. A release is considered to preserve k-anonymity if the record corresponding to any person cannot be distinguished from that of at least k − 1 other individuals whose information also appears in the release. In order to achieve k-anonymity, we propose an unsupervised learning framework. We further show an instantiation of the framework, which leads to an exemplar-based clustering algorithm for practical applications, and report promising results.
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Li, W. (2014). A Unified Framework for Privacy Preserving Data Clustering. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_40
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DOI: https://doi.org/10.1007/978-3-319-12637-1_40
Publisher Name: Springer, Cham
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