Welcome to the resource topic for 2023/1098
Title:
\textsf{Asterisk}: Super-fast MPC with a Friend
Authors: Banashri Karmakar, Nishat Koti, Arpita Patra, Sikhar Patranabis, Protik Paul, Divya Ravi
Abstract:Secure multiparty computation (MPC) enables privacy-preserving collaborative computation over sensitive data held by multiple mutually distrusting parties. Unfortunately, in the most natural setting where a majority of the parties are maliciously corrupt (also called the \textit{dishonest majority} setting), traditional MPC protocols incur high overheads and offer weaker security guarantees than are desirable for practical applications. In this paper, we explore the possibility of circumventing these drawbacks and achieving practically efficient dishonest majority MPC protocols with strong security guarantees by assuming an additional semi-honest, non-colluding helper party \textsf{HP}. We believe that this is a more realistic alternative to assuming an honest majority, since many real-world applications of MPC involving potentially large numbers of parties (such as secure auctions and dark pools) are typically enabled by a central entity that can be modeled as the \textsf{HP}.
In the above model, we are the first to design, implement and benchmark a practically-efficient and general multi-party framework, $\textsf{Asterisk}$, which achieves the strong security guarantee of $\textit{fairness}$ (either all parties learn the output or none do), scales to hundreds of parties, outperforms all existing dishonest majority MPC protocols, and is, in fact, competitive with state-of-the-art honest majority MPC protocols. Our experiments show that $\textsf{Asterisk}$ achieves $900-1200\times$ speedup in preprocessing as compared to the best dishonest majority MPC protocols, and supports $100$-party evaluation of a circuit with $10^6$ multiplication gates in under $2$ minutes. We also implement and benchmark practically efficient and highly scalable instances of two applications, namely privacy-preserving secure auctions and dark pools, using $\textsf{Asterisk}$ as the building block. This showcases the effectiveness of $\textsf{Asterisk}$ in enabling real-world privacy-preserving applications with strong efficiency and security guarantees.
ePrint: https://eprint.iacr.org/2023/1098
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