MPClan: Protocol Suite for Privacy-Conscious Computations

Abstract

The growing volumes of data being collected and its analysis to provide better services are creating worries about digital privacy. To address privacy concerns and give practical solutions, the literature has relied on secure multiparty computation techniques. However, recent research over rings has mostly focused on the small-party honest-majority setting of up to four parties tolerating single corruption, noting efficiency concerns. In this work, we extend the strategies to support higher resiliency in an honest-majority setting with efficiency of the online phase at the centre stage. Our semi-honest protocol improves the online communication of the protocol of Damgård and Nielsen (CRYPTO’07) without inflating the overall communication. It also allows shutting down almost half of the parties in the online phase, thereby saving up to 50% in the system’s operational costs. Our maliciously secure protocol also enjoys similar benefits and requires only half of the parties, except for one-time verification towards the end, and provides security with fairness. To showcase the practicality of the designed protocols, we benchmark popular applications such as deep neural networks, graph neural networks, genome sequence matching, and biometric matching using prototype implementations. Our protocols, in addition to improved communication, aid in bringing up to 60–80% savings in monetary cost over prior work.