Paper 2022/675

MPClan: Protocol Suite for Privacy-Conscious Computations

Nishat Koti, Indian Institute of Science Bangalore
Shravani Patil, Indian Institute of Science Bangalore
Arpita Patra, Indian Institute of Science Bangalore
Ajith Suresh, Technical University of Darmstadt

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. 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 an honest-majority setting with efficiency at the center stage. Cast 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 one-time verification, towards the end. 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 improved protocols aid in bringing up to 60-80$\%$ savings in monetary cost over prior work.

Available format(s)
Cryptographic protocols
Publication info
multi-party computation honest majority privacy-preserving machine learning biometric matching neural networks
Contact author(s)
kotis @ iisc ac in
shravanip @ iisc ac in
arpita @ iisc ac in
suresh @ encrypto cs tu-darmstadt de
2022-06-24: revised
2022-05-30: received
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Creative Commons Attribution


      author = {Nishat Koti and Shravani Patil and Arpita Patra and Ajith Suresh},
      title = {MPClan: Protocol Suite for Privacy-Conscious Computations},
      howpublished = {Cryptology ePrint Archive, Paper 2022/675},
      year = {2022},
      note = {\url{}},
      url = {}
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