Scalar-product based secure multi-party computation
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Secure multiparty computation (SMC) enables parties to carry out distributed computing tasks, guaranteeing no additional private information is revealed, other than what can be inferred from each participant's inputs and outputs, and the correctness of their outputs. This thesis focuses on designing practical SMC protocols that are applicable to real world problems. The author shows that SMC on any polynomial evaluation can be solved with the proposed scalar-product based protocols and the help of additive sharing. Besides that, the author enables statistical analysis and privacy preserving distributed mining, e.g. Na¨ıve Bayesian classification on horizontally partitioned data, by designing secure protocols based on scalar-product protocols. A novel secure auction protocol is proposed by exploring the trade-off between the strength of privacy protection and the protocol complexity.
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