Zero-Knowledge AI Inference with High Precision
Arman Riasi, Haodi Wang, Rouzbeh Behnia, Viet Vo, Thang Hoang
2025年份
3顶会引用
摘要
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AIHeng Jin, Chaoyu Zhang, Hexuan Yu, Shanghao Shi 等USENIX Security 2026 · 被引用 4 次
- Efficiently Provable Approximations for Non-Polynomial FunctionsSriram Sridhar, Shravan Srinivasan, Dimitrios Papadopoulos, Charalampos PapamanthouUSENIX Security 2026 · 被引用 1 次
- MINIM: Privacy-Aware Minimal View for Agents via Trusted Local SanitizationHexuan Yu, Chaoyu Zhang, Heng Jin, Shanghao Shi 等ICML 2026
它引用的顶会 Paper22
- Stealing Machine Learning Models via Prediction APIsFlorian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter 等USENIX Security 2016 · 被引用 2,088 次
- Bulletproofs: Short Proofs for Confidential Transactions and MoreBenedikt Bünz, Jonathan Bootle, Dan Boneh, Andrew Poelstra 等S&P 2018 · 被引用 1,285 次
- Stealing Hyperparameters in Machine LearningBinghui Wang, Neil Zhenqiang GongS&P 2018 · 被引用 504 次
- Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine LearningChenkai Weng, Kang Yang, Xiang Xie, Jonathan Katz 等USENIX Security 2021 · 被引用 161 次
- DIZK: A Distributed Zero Knowledge Proof SystemHoward Wu, Wenting Zheng, Alessandro Chiesa, Raluca Ada Popa 等USENIX Security 2018 · 被引用 152 次
相关 Paper
- zkGPT: An Efficient Non-interactive Zero-knowledge Proof Framework for LLM InferenceWenjie Qu, Yijun Sun, Xuanming Liu, Tao Lu 等USENIX Security 2025
- zkSaaS: Zero-Knowledge SNARKs as a ServiceSanjam Garg, Aarushi Goel, Abhishek Jain, Guru-Vamsi Policharla 等USENIX Security 2023
- VerfCNN, Optimal Complexity zkSNARK for Convolutional Neural NetworksWenjie Qu, Yanpei Guo, Yue Ying, Jiaheng ZhangS&P 2026 · 被引用 4 次
- DeepProve: Verifiable End-to-End Large Language Model InferenceNicolas Gailly, Ismael Hishon-Rezaizadeh, Tianyi Liu, Nicholas Mainardi 等CCS 2026
- Zero-Knowledge Location Privacy via Accurate Floating-Point SNARKsJens Ernstberger, Chengru Zhang, Luca Ciprian, Philipp Jovanovic 等S&P 2025
