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Pinocchio: Nearly Practical Verifiable Computation
Open accessAbstract
To instill greater confidence in computations outsourced to the cloud, clients should be able to verify the correctness of the results returned. To this end, we introduce Pinocchio, a built system for efficiently verifying general computations while relying only on cryptographic assumptions. With Pinocchio, the client creates a public evaluation key to describe her computation; this setup is proportional to evaluating the computation once. The worker then evaluates the computation on a particular input and uses the evaluation key to produce a proof of correctness. The proof is only 288 bytes, regardless of the computation performed or the size of the inputs and outputs. Anyone can use a public verification key to check the proof. Crucially, our evaluation on seven applications demonstrates that Pinocchio is efficient in practice too. Pinocchio's verification time is typically 10ms: 5-7 orders of magnitude less than previous work; indeed Pinocchio is the first general-purpose system to demonstrate verification cheaper than native execution (for some apps). Pinocchio also reduces the worker's proof effort by an additional 19-60x. As an additional feature, Pinocchio generalizes to zero-knowledge proofs at a negligible cost over the base protocol. Finally, to aid development, Pinocchio provides an end-to-end toolchain that compiles a subset of C into programs that implement the verifiable computation protocol.
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“In this direction, [118] proposed a multi-party protocol for constructing the public parameters of the Pinocchio zk-SNARK [95], which is the baseline for Zcash [17].”
From Privacy-Preserving Solutions for Blockchain: Review and Challenges (2019) · cites this paper as [95]
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“Pinocchio by Parno et al. in 2013 which applies ZKP to reallife use cases [128]”
From A Survey of Self-Sovereign Identity Ecosystem (2021) · cites this paper as [128]
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“Man-in-the-Middle attacks [84], [85] intercept the models exchanged between the clients and the server and replace them with malicious model updates.”
From Vulnerabilities in federated learning (2021) · cites this paper as [85]
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Tramèr, F., Zhang, F., Lin, H., Hubaux, J., Juels, A., & Shi, E. (2017) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp.2017.28 · Cited by 88
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Metadata
- Authors
- Parno, B., Howell, J., Gentry, C., & Raykova, M.
- Affiliations
- Microsoft (United States) (US)
- IBM (United States) (US)
- Venue
- 2013 IEEE Symposium on Security and Privacy
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 238-252
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W2018746447
- Cited by
- 864
- References
- 72
- Citations by year
- 2026: 40 · 2025: 44 · 2024: 68 · 2023: 70 · 2022: 101 · 2021: 95 · 2020: 82 · 2019: 68 · 2018: 54 · 2017: 52 · 2016: 55 · 2015: 51 · 2014: 54 · 2013: 25
- FWCI
- 39.83
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Symposium on Security and Privacy (S&P) (publishedVersion)
- Funders
- National Science Foundation (1017660)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Keywords
- verifiable computation, outsourced computation, cloud computing, correctness proofs, cryptographic assumptions, verification efficiency, zero-knowledge proofs, Pinocchio
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