Poster MPClan:: Protocol Suite for Privacy-Conscious Computations

The growing volumes of data collected and its analysis to provide better services create worries about digital privacy. The literature has relied on secure multiparty computation techniques to address privacy concerns and give practical solutions. However, recent research has mostly focused on the small-party honest-majority setting of up to four parties, noting efficiency concerns. In this work, we extend the strategies to support a larger number of participants in honest-majority setting with efficiency at the center stage. Designed in the preprocessing paradigm, our semi-honest protocol improves the online complexity of the decade-old state-of-the-art protocol of Damgård and Nielson (CRYPTO'07). In addition to having an improved online communication cost, we can shut 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 a one-time verification, towards the end. We benchmark popular applications such as deep neural networks, graph neural networks and genome sequence matching using prototype implementations to showcase the practicality of the designed protocols. Our improved protocols aid in bringing up to 60-80% savings in monetary cost over prior work.