Efficient Privacy Preserving K-Means Clustering

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Efficient Privacy Preserving K-Means Clustering
Abstract. This paper introduces an efficient privacy-preserving protocol for distributed K-means clustering over an arbitrary partitioned data, shared among N parties. Clustering is one of the fundamental algorithms used in the field of data mining. Advances in data acquisition methodologies have resulted in collection and storage of vast quantities of user's personal data. For mutual benefit, organizations tend to share their data for analytical purposes, thus raising privacy concerns for the users. Over the years, numerous attempts have been made to introduce privacy and security at the expense of massive additional communication costs. The approaches suggested in the literature make use of the cryptographic protocols such as Secure Multiparty Computation (SMC) and/or homomorphic encryption schemes like Paillier's encryption. Methods using such schemes have proven communication overheads. And in practice are found to be slower by a factor of more than 106 . In light of the ...
Maneesh Upmanyu, Anoop M. Namboodiri, Kannan Srina
Added 14 Feb 2011
Updated 14 Feb 2011
Type Journal
Year 2010
Authors Maneesh Upmanyu, Anoop M. Namboodiri, Kannan Srinathan, C. V. Jawahar
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