Privacy-Preserving Protocols for Eigenvector Computation

Details Privacy-Preserving Protocols for Eigenvector Computation Privacy-Preserving Protocols for Eigenvector Computation

2010-02-19

arxiv.org/abs/1002.3671v3

Proceedings of ECML/PKDD Workshop on Privacy and Security issues in Data Mining and Machine Learning, 2010

Computer Science

Cryptography and Security

14 pages

Scientific paper

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.

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