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Lecture notes in computer science

Scalable Multiparty Computation from Non-linear Secret Sharing

Sanjam Garg, Abhishek Jain, Pratyay Mukherjee, Mingyuan Wang

Published Jan 1, 2024• 2 citations
Publisher
DOI
10.1007/978-3-031-68397-8_12

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We revisit shuffle protocols for Shamir secret sharing. Existing constructions either produce non-uniform shuffles or incur high communication and round complexity, sometimes exponential in the number of parties. We propose two new shuffle protocols that achieve uniform shuffling with communication complexity O ( k + l log k n 2 m log m ) for an m × l matrix shared among n parties, where k ≤ m is a tunable parameter. The first protocol is concretely efficient, while the second achieves the best-known O ( n m l ) online communication and O ( n ) rounds. Experiments show significant improvements in online efficiency and total cost over prior work. Our key technical ingredient is a novel permutation sharing technique that represents permutations via smaller permutation matrices, making their application significantly more efficient. The first protocol applies independent secret permutations sequentially, while the second builds on shuffle correlation to achieve optimal online complexity. We further extend shuffle correlation to support guaranteed output delivery with linear online communication, yielding SLIDE (shuffle Shamir secret shares uniformly with linear online communication and guaranteed output delivery), the first protocol achieving both O ( n m l ) online communication and guaranteed output delivery. Our constructions rely only on basic Shamir secret sharing over any field F with | F | > n . As shuffling is a fundamental primitive for multiparty computation tasks such as sorting and oblivious data structures, our results enable more efficient and scalable secure computation in practice.

arXiv:2608.27129•Journal of Computer Security•Sep 23, 2026•0 citations
With respect to a special class of access structures based on connectivity of graphs, we start from a linear secret sharing scheme and turn it into a secret sharing scheme with perfect security and exponentially small error probability by randomizing the reconstruction algorithm through random walks on graphs. It reduces the polynomial work space to logarithmic. Then we build the corresponding statistical multiparty computation protocol by using the secret sharing scheme. The results of this paper also imply the inherent connections and influences among secret sharing, randomized algorithms, and secure multiparty computation.
We revisit shuffle protocols for Shamir secret sharing. Existing constructions either produce non-uniform shuffles or incur high communication and round complexity, sometimes exponential in the number of parties. We propose two new shuffle protocols that achieve uniform shuffling with communication complexity O ( k + l log k n 2 m log m ) for an m × l matrix shared among n parties, where k ≤ m is a tunable parameter. The first protocol is concretely efficient, while the second achieves the best-known O ( n m l ) online communication and O ( n ) rounds. Experiments show significant improvements in online efficiency and total cost over prior work. Our key technical ingredient is a novel permutation sharing technique that represents permutations via smaller permutation matrices, making their application significantly more efficient. The first protocol applies independent secret permutations sequentially, while the second builds on shuffle correlation to achieve optimal online complexity. We further extend shuffle correlation to support guaranteed output delivery with linear online communication, yielding SLIDE (shuffle Shamir secret shares uniformly with linear online communication and guaranteed output delivery), the first protocol achieving both O ( n m l ) online communication and guaranteed output delivery. Our constructions rely only on basic Shamir secret sharing over any field F with | F | > n . As shuffling is a fundamental primitive for multiparty computation tasks such as sorting and oblivious data structures, our results enable more efficient and scalable secure computation in practice.