Zero-Knowledge AI Inference with High Precision
Artificial Intelligence as a Service (AIaaS) enables users to query a model hosted by a service provider and receive inference results from a pre-trained model. Although AIaaS makes artificial intelligence more accessible, particularly for resource-limited users, it also raises verifiability and privacy concerns for the client and server, respectively. While zero-knowledge proof techniques can address these concerns simultaneously, they incur high proving costs due to the non-linear operations involved in AI inference and suffer from precision loss because they rely on fixed-point representations to model real numbers.
Authors
- Haodi Wang (ORCID: https://orcid.org/0000-0002-5453-4126)
- Thang Hoang (ORCID: https://orcid.org/0000-0003-2229-3863)
- Arman Riasi (ORCID: https://orcid.org/0009-0008-9502-7500)
- Rouzbeh Behnia (ORCID: https://orcid.org/0000-0003-0423-7606)
- Viet Vo (ORCID: https://orcid.org/0000-0002-5984-7981)
Institutions
- City University of Hong Kong (HK)
- University of South Florida (US)
- Swinburne University of Technology (AU)
- Virginia Tech (US)
Publication Details
- Published
- 2025-11-19
- DOI
- https://doi.org/10.1145/3719027.3765056
- Primary Topic
- Adversarial Robustness in Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00