Why and How zk-SNARK Works
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Abstract (arXiv)
Despite the existence of multiple great resources on zk-SNARK construction, from original papers to explainers, due to the sheer number of moving parts the subject remains a black box for many. While some pieces of the puzzle are given one can not see the full picture without the missing ones. Hence the focus of this work is to shed light onto the topic with a straightforward and clean approach based on examples and answering many whys along the way so that more individuals can appreciate the state of the art technology, its innovators and ultimately the beauty of math. Paper's contribution is a simplistic exposition with a sufficient and gradually increasing level of complexity, necessary to understand zk-SNARK without any prerequisite knowledge of the subject, cryptography or advanced math. The primary goal is not only to explain how it works but why it works and how it came to be this way.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jun 2019.
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Citing papers per year: 2024: 2; 2025: 2; 2026: 1.
5 papers.
- 2026Intelligent AI Delegationcited by 18
- 2024Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactionscited by 6
- 2025ZKTorch: Compiling ML Inference to Zero-Knowledge Proofs via Parallel Proof Accumulationcited by 3
- 2024VerifiableFL: Verifiable Claims for Federated Learning using Exclavescited by 1
- 2025On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigationcited by 0
- 2026Intelligent AI Delegation12 Feb 2026
- 2025On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation15 Oct 2025
- 2025ZKTorch: Compiling ML Inference to Zero-Knowledge Proofs via Parallel Proof Accumulation9 Jul 2025
- 2024VerifiableFL: Verifiable Claims for Federated Learning using Exclaves13 Dec 2024
- 2024Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactions9 Feb 2024