Abstract
This paper presents a framework for secure Expectation Maximization (EM) clustering construction over partitioned data. It is assumed that data is distributed among several (more than two) parties either horizontally or vertically, such that for mutual benefits all the parties are willing to identify clusters on their data as a whole, but for privacy restrictions, they avoid to share their datasets. To this end, in this study general algorithms based on secure sum is proposed to securely compute the desired criteria in constructing clusters’ scheme.
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Acknowledgment
This work was supported by the H2020 EU funded project C3ISP [GA #700294].
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Hamidi, M., Sheikhalishahi, M., Martinelli, F. (2019). Privacy Preserving Expectation Maximization (EM) Clustering Construction. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_31
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DOI: https://doi.org/10.1007/978-3-319-94649-8_31
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