Rotaru, Dragos A

English

A set of parties want to compute a function $mathcal{F}$ over their inputswithout revealing them, learning only the output of $mathcal{F}$. This is the traditional scenario introduced to show what secure Multi-Party Computation (MPC) can achieve: computing on encrypted data. Due to the initial theoretical papers appearing in the beginning of 80s describing basic protocols to achieve MPC, it has now become a hot topic in the cryptographic community where we can see dozens of startups finding good use-cases such as machine learning on encrypted data as well as high quality research constantly pushing the field's boundaries.The goal of this thesis is to improve on dishonest majority MPC where all but one of the parties can arbitrarily deviate from the protocol and still ensure input privacy of the honest parties

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Optimizing Secure Multiparty Computation Protocols for Dishonest Majority

https://research-information.bris.ac.uk/files/254378099/Final_Copy_2020_05_12_Rotaru_D_PhD.pdf

Optimizing Secure Multiparty Computation Protocols for Dishonest Majority

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