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CCS2021Top-tier venue

QuickSilver: Efficient and Affordable Zero-Knowledge Proofs for Circuits and Polynomials over Any Field

Kang Yang, Pratik Sarkar, Chenkai Weng, Xiao Wang

2021Year
2Citations
40Top-tier citations

Abstract

Zero-knowledge (ZK) proofs with an optimal memory footprint have attracted a lot of attention, because such protocols can easily prove very large computation with a small memory requirement. Such ZK protocol only needs O(M) memory for both parties, where M is the memory required to verify the statement in the clear. In this paper, we propose several new constant-round ZK protocols in this setting, which improve the concrete efficiency and, at the same time, enable sublinear amortized communication for circuits with some notion of relaxed uniformity. In the circuit-based model, where the computation is represented as a circuit over a field, our ZK protocol achieves a communication complexity of 1 field element per non-linear gate for any field size while keeping the computation very cheap. We implemented our protocol, which shows extremely high efficiency and affordability. Compared to the previous best-known implementation, we achieve 6x--7x improvement in computation and 3x--7x improvement in communication. When running on intro-level AWS instances, our protocol only needs one US dollar to prove one trillion AND gates (or 2.5 US dollars for one trillion multiplication gates over a 61-bit field). In the setting where part of the computation can be represented as a set of polynomials with a "degree-separated" format, we can achieve communication sublinear to the polynomial size: the communication only depends on the total number of distinct variables in all the polynomials and the highest degree of all polynomials, independent of the number of multiplications to compute all polynomials. Using the improved ZK protocol, we can prove matrix multiplication with communication proportional to the input size, rather than the number of multiplications. Proving the multiplication of two 1024 x 1024 matrices, our implementation, with one thread and 1 GB of memory, only needs 10 seconds and communicates 25 MB.

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