Privacy Preserving Clustering using Fully Homomorphic Encryption

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Provided by: Indian Institute of Technology Kanpur
Topic: Big Data
Format: PDF
In this paper, the authors focus on the privacy preserving scheme for distributed k-means clustering. Various techniques have been suggested in the literature for privacy preserving distributed clustering which is either cryptography based or non-cryptography based. In the non-cryptography based techniques, there is a trade-off between privacy and accuracy. Whereas the cryptography based techniques provide higher level of privacy without loss of accuracy. However, existing cryptography based techniques are based on the Yao's Garbled circuit which incurs very high computational and communicational overheads and hence not scalable.
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