Abstract
This paper presents a framework for constructing a hierarchical categorical clustering algorithm on horizontal and vertical partitioned dataset. It is assumed that data is distributed between more than two parties, such that for general benefits all are willing to detect the clusters on whole dataset, but for privacy concerns, they avoid to share the original datasets. To this end, we propose algorithms based on distributed secure sum and secure number comparison protocols to securely compute the desired criteria in constructing clusters’ scheme without revealing private data.
This work has been supported by the H2020 EU funded project C3ISP [GA #700294].
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Sheikhalishahi, M., Martinelli, F. (2017). Privacy Preserving Hierarchical Clustering over Multi-party Data Distribution. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10656. Springer, Cham. https://doi.org/10.1007/978-3-319-72389-1_42
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DOI: https://doi.org/10.1007/978-3-319-72389-1_42
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