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A privacy-preserving K-means clustering is an application of secure computation that allows parties, each holding a set of private data points, to cluster their their combined data sets without revealing anything except for the cluster centers.
Jun 16, 2020
Abstract. We study the problem of r-anonymized clustering and give a k-means type extension. The problem is partition a set of objects into k different groups ...
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What is privacy preserving K clustering?
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It partitions m observations into k clusters in which each observation belongs to the cluster with the nearest mean. The objective is to minimize the total ...
We study the problem of r-anonymized clustering and give a k-means type extension. The problem is partition a set of objects into k different groups by ...
Most of researches on privacy preservation in clustering are developed for k-means clustering algorithm, by applying the secure multi-party computation ...
In this paper, we use the concept of the k-means algorithm and propose a Reversible Privacy-Preserving k-means Clustering (kRPP) algorithm for protecting the ...
Sep 22, 2020 · This paper focuses in Privacy Preserving Machine Learning applied to K-means using recent protocols from the field of criptography.
Missing: Type Extension.
In this paper, we propose the first locally differentially private K-means mechanism under this distributed scenario.
We present an efficient privacy-preserving K-means clustering algorithm based on replicated secret sharing with honest-majority in the semi-honest model.
In this paper, we present the design and analysis of a privacy-preserving k-means clustering algorithm, where only the cluster means at the various steps of the ...