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zkLLM: Zero Knowledge Proofs for Large Language Models

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Sun, Haochen, Li, Jason, Zhang, Hongyang·2024·Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security
DOI 10.1145/3658644.3670334
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Abstract

The recent surge in artificial intelligence (AI), characterized by the prominence of large language models (LLMs), has ushered in fundamental transformations across the globe. However, alongside these advancements, concerns surrounding the legitimacy of LLMs have grown, posing legal challenges to their extensive applications. Compounding these concerns, the parameters of LLMs are often treated as intellectual property, restricting direct investigations.

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Cited by

Sorted by citations

Authors
Sun, H., Li, J., & Zhang, H.
Affiliations
  • University of Waterloo (CA)
  • University of Waterloo, Waterloo, Ontario, Canada
ORCID
Venue
Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security
Publisher
ACM
Published
2024-12-02
Pages
4405-4419
ISSN
1543-7221 (ISSN-L)
DOI
10.1145/3658644.3670334
OpenAlex
W4405183082
Cited by
55
References
4
Citations by year
2026: 35 · 2025: 19 · 2024: 1
FWCI
16.26
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
NSERC Discovery Grant (RGPIN-2022-03215, DGECR-2022-00357)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
  • Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
Keywords
zero-knowledge proofs, large language models, LLM auditing, intellectual property

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