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Secure Multiparty Computation and Secret SharingJuly 2015
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Abstract
In a data-driven society, individuals and companies encounter numerous situations where private information is an important resource. How can parties handle confidential data if they do not trust everyone involved? This text is the first to present a comprehensive treatment of unconditionally secure techniques for multiparty computation (MPC) and secret sharing. In a secure MPC, each party possesses some private data, while secret sharing provides a way for one party to spread information on a secret such that all parties together hold full information, yet no single party has all the information. The authors present basic feasibility results from the last 30 years, generalizations to arbitrary access structures using linear secret sharing, some recent techniques for efficiency improvements, and a general treatment of the theory of secret sharing, focusing on asymptotic results with interesting applications related to MPC.
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- Kapusta K, Rambaud M and Memmi G Revisiting Shared Data Protection Against Key Exposure Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, (165-177)
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- Catrina O Optimization and Tradeoffs in Secure Floating-Point Computation Proceedings of the 6th Conference on the Engineering of Computer Based Systems, (1-10)
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- Eshun S and Palmieri P A privacy-preserving protocol for indoor wi-fi localization Proceedings of the 16th ACM International Conference on Computing Frontiers, (380-385)
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- Lyu L, He X, Law Y and Palaniswami M Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, (1219-1228)
- Ah-Fat P and Huth M Secure Multi-party Computation Proceedings of the 6th International Conference on Principles of Security and Trust - Volume 10204, (71-92)
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