Paper 2025/572

Zinnia: An Expressive and Efficient Tensor-Oriented Zero-Knowledge Programming Framework

Abstract

Zero-knowledge proofs (ZKPs) are cryptographic protocols that enable a prover to convince a verifier of a statement's truth without revealing any details beyond its validity. Typically, the statement is encoded as an arithmetic circuit, and allows the prover to demonstrate that the circuit evaluates to true without revealing its inputs. Despite their potential to enhance privacy and security, ZKPs are difficult to write and optimize, limiting their adoption in machine learning and data science. To address these challenges, we introduce Zinnia, a zero-knowledge programming framework with high utility, expressiveness and efficiency for tensor-oriented computation. Zinnia provides a high-level programming language that enables developers to easily write ZKP programs, and it employs a novel symbolic execution-inspired approach to extracting semantics from these programs to generate arithmetic circuits. Zinnia supports tensor-oriented computations and provides a rich set of programming constructs, optimizations, and a powerful static type system for expressing and optimizing complex logic. We evaluate Zinnia across 25 real-world programming tasks and a user study, comparing it to existing solutions, including DSLs and zkVMs (Halo2, SP1, and RISC0). Our results demonstrate that Zinnia outperforms these baselines in utility, expressiveness, and efficiency, with a statistically significant reduction in development time, $$ shorter code length, 19.3% smaller circuit size, and up to $$ faster proving time compared to zkVMs, paving the way for practical ZKP applications in various domains.