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Erschienen in:
Hyperspherical Weight Uncertainty in Neural Networks Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Efficient Privacy Preserving Distributed K-Means for Non-IID Data

verfasst von : André Brandão, Ricardo Mendes, João P. Vilela

Erschienen in: Advances in Intelligent Data Analysis XIX

Verlag: Springer International Publishing

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Abstract

Privacy is becoming a crucial requirement in many machine learning systems. In this paper we introduce an efficient and secure distributed K-Means algorithm, that is robust to non-IID data. The base idea of our proposal consists in each client computing the K-Means algorithm locally, with a variable number of clusters. The server will use the resultant centroids to apply the K-Means algorithm again, discovering the global centroids. To maintain the client’s privacy, homomorphic encryption and secure aggregation is used in the process of learning the global centroids. This algorithm is efficient and reduces transmission costs, since only the local centroids are used to find the global centroids. In our experimental evaluation, we demonstrate that our strategy achieves a similar performance to the centralized version even in cases where the data follows an extreme non-IID form.

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Vorheriges Kapitel The Compromise of Data Privacy in Predictive Performance
Fußnoten
2
We assume a constant time complexity for multiplication between the encrypted centroids and the plaintext global centroids, according to [20].
 
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Metadaten
Titel
Efficient Privacy Preserving Distributed K-Means for Non-IID Data
verfasst von
André Brandão
Ricardo Mendes
João P. Vilela
Copyright-Jahr
2021
Buch
Advances in Intelligent Data Analysis XIX

Print ISBN: 978-3-030-74250-8

Electronic ISBN: 978-3-030-74251-5

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-74251-5_35