CRS-Updatable Asymmetric Quasi-Adaptive NIZK Arguments

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Abstract

A critical aspect for the practical use of non-interactive zero-knowledge (NIZK) arguments in the common reference string (CRS) model is the demand for a trusted setup, i.e., a trusted generation of the CRS. Recently, motivated by its increased use in real-world applications, there has been a growing interest in concepts that allow to reduce the trust in this setup. In particular one demands that the zero-knowledge and ideally also the soundness property hold even when the CRS generation is subverted. One important line of work in this direction is the so-called updatable CRS for NIZK by Groth et al. (CRYPTO’18). The basic idea is that everyone can update a CRS and there is a way to check the correctness of an update. This guarantees that if at least one operation (the generation or one update) have been performed honestly, the zero-knowledge and the soundness properties hold. Later, Lipmaa (SCN’20) adopted this notion of updatable CRS to quasi-adaptive NIZK (QA-NIZK) arguments. In this work, we continue the study of CRS-updatable QA-NIZK and analyse the most efficient asymmetric QA-NIZKs by González et al. (ASIACRYPT’15) in a setting where the CRS is fully subverted and propose an updatable version of it. In contrast to the updatable QA-NIZK by Lipmaa (SCN’20) which represents a symmetric QA-NIZK and requires a new non-standard knowledge assumption for the subversion zero-knowledge property, our technique to construct updatable asymmetric QA-NIZK is under a well-known standard knowledge assumption, i.e., the Bilinear Diffie-Hellman Knowledge of Exponents assumption. Furthermore, we show the knowledge soundness of the (updatable) asymmetric QA-NIZKs, an open problem posed by Lipmaa, which makes them compatible with modular zk-SNARK frameworks such as LegoSNARK by Campanelli et al. (ACM CCS’19).

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APA

Abdolmaleki, B., & Slamanig, D. (2022). CRS-Updatable Asymmetric Quasi-Adaptive NIZK Arguments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13774 LNCS, pp. 3–25). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-22912-1_1

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