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2017 | OriginalPaper | Buchkapitel

Privacy Preserving Hierarchical Clustering over Multi-party Data Distribution

verfasst von : Mina Sheikhalishahi, Fabio Martinelli

Erschienen in: Security, Privacy, and Anonymity in Computation, Communication, and Storage

Verlag: Springer International Publishing

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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.

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Titel: Privacy Preserving Hierarchical Clustering over Multi-party Data Distribution
verfasst von: Mina Sheikhalishahi
Fabio Martinelli
Verlag: Springer International Publishing
Buch: Security, Privacy, and Anonymity in Computation, Communication, and Storage
Print ISBN: 978-3-319-72388-4

Electronic ISBN: 978-3-319-72389-1

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-72389-1_42

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