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Zero-Knowledge AI Inference with High Precision

Year: 2025

Type: conference-paper

Abstract: 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.

Source: ACM SIGSAC Conference on Computer and Communications Security (CCS)

DOI: 10.1145/3719027.3765056

Authors: Arman Riasi, Haodi Wang, Rouzbeh Behnia, Viet Vo, Thang Hoang

Institutions: Virginia Tech, City University of Hong Kong, University of South Florida, Swinburne University of Technology

Language: English

Topic: Cryptography and Data Security

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4416549585

FWCI:

Cites: 45

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