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CORR
2010
Springer

Privacy-Preserving Protocols for Eigenvector Computation

11 years 1 months ago
Privacy-Preserving Protocols for Eigenvector Computation
Abstract. In this paper, we present a protocol for computing the principal eigenvector of a collection of data matrices belonging to multiple semi-honest parties with privacy constraints. Our proposed protocol is based on secure multi-party computation with a semi-honest arbitrator who deals with data encrypted by the other parties using an additive homomorphic cryptosystem. We augment the protocol with randomization and obfuscation to make it difficult for any party to estimate properties of the data belonging to other parties from the intermediate steps. The previous approaches towards this problem were based on expensive QR decomposition of correlation matrices, we present an efficient algorithm using the power iteration method. We analyze the protocol for correctness, security, and efficiency.
Manas A. Pathak, Bhiksha Raj
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CORR
Authors Manas A. Pathak, Bhiksha Raj
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