2026-04-17T07:59:49Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/729242026-03-31T17:21:59Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Amine, Oussama author Baghery, Karim author Pindado, Zaira author Ràfols, Carla author 2026-03-30T10:29:43Z 2026-03-30T10:29:43Z 2024 2026-03-30T10:29:43Z Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are the most efficient proof systems in terms of proof size and verification. Currently, Groth's scheme from EUROCRYPT 2016, Groth16 , is the state-of-the-art and is widely deployed in practice. Groth16 is originally proven to achieve knowledge soundness, which does not guarantee the non-malleability of proofs. There has been considerable progress in presenting new zk-SNARKs or modifying Groth16 to efficiently achieve strong Simulation extractability, which is shown to be a necessary requirement in some applications. In this paper, we revise the Random oracle based variant of Groth16 proposed by Bowe and Gabizon, BG18, the most efficient one in terms of prover efficiency and CRS size among the candidates, and present a more efficient variant that saves 2 pairings in the verification and 1 group element in the proof. This supersedes our preliminary construction, presented in CANS 2020 (Baghery et al. in CANS 20, volume 12579 of LNCS, Springer, Heidelberg. pp 453-461, 2020), which saved 1 pairing in the verification, and was proven in the generic group model. Our new construction also improves on BG18 in that our proofs are in the algebraic group model with Random Oracles and reduces security to standard computational assumptions in bilinear groups (as opposed to using the full power of the generic group model (GGM)). We implement our proposed simulation extractable zk-SNARK (SE zk-SNARK) along with BG18 in the Arkworks library, and compare the efficiency of our scheme with some related works. Our empirical experiences confirm that our SE zk-SNARK is more efficient than all previous simulation extractable (SE) schemes in most dimensions and it has very close efficiency to the original Groth16 . We thank Alonso González for his helpful discussions and the reviewers for their valuable comments. Karim Baghery has been supported in part by the Defense Advanced Research Projects Agency (DARPA) under contract No. HR001120C0085, by the FWO under an Odysseus project GOH9718N, and by CyberSecurity Research Flanders with reference number VR20192203. Carla Ràfols was partially supported by Project RTI2018-102112-B-I00 (AEI / FEDER, UE). https://hdl.handle.net/10230/72924 NIZK Zk-SNARK Strong simulation extractability Algebraic group model Random oracle model Simulation extractable versions of Groth's zk-SNARK revisited