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The growing volumes of data being collected and its analysis to providebetter services are creating worries about digital privacy. To address privacyconcerns and give practical solutions, the literature has relied on securemultiparty computation. However, recent research has mostly focused on thesmall-party honest-majority setting of up to four parties, noting efficiencyconcerns. In this work, we extend the strategies to support a larger number ofparticipants in an honest-majority setting with efficiency at the center stage.  Cast in the preprocessing paradigm, our semi-honest protocol improves theonline complexity of the decade-old state-of-the-art protocol of Damg\aa rd andNielson (CRYPTO'07). In addition to having an improved online communicationcost, we can shut down almost half of the parties in the online phase, therebysaving up to 50% in the system's operational costs. Our maliciously secureprotocol also enjoys similar benefits and requires only half of the parties,except for one-time verification, towards the end.  To showcase the practicality of the designed protocols, we benchmark popularapplications such as deep neural networks, graph neural networks, genomesequence matching, and biometric matching using prototype implementations. Ourimproved protocols aid in bringing up to 60-80% savings in monetary cost overprior work.

MPClan: Protocol Suite for Privacy-Conscious Computations