In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results-whether over task accuracy, bias evaluations, or safety checks-are traditionally impossible to verify by a model end-user without the costly or impossible process of re-performing the benchmark on black-box model outputs. This work presents a method of verifiable model evaluation using model inference through zkSNARKs. The resulting zero-knowledge computational proofs of model outputs over datasets can be packaged into verifiable evaluation attestations showing that models with fixed private weights achieve stated performance or fairness metrics over public inputs. We present a flexible proving system that enables verifiable attestations to be performed on any standard neural network model with varying compute requirements. For the first time, we demonstrate this across a sample of real-world models and highlight key challenges and design solutions. This presents a new transparency paradigm in the verifiable evaluation of private models.
A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）；School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子工程与光电技术学院）
Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey
School of Cyberspace Science and Technology, Beijing Institute of Technology（信息科学与技术学院，北京理工大学）；School of Computer Science, The University of Auckland（计算机科学学院，奥克兰大学）；Southeast Institute of Information Technology, Beijing Institute of Technology（信息科技东南研究所，北京理工大学）；Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education（大数据知识工程重点实验室（合肥工业大学），教育部）；School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院，合肥工业大学）；Anhui Province Key Laboratory of Industry Safety and Emergency Technology（安徽省产业安全与应急技术重点实验室）；Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology)（安徽省智能互联系统实验室（合肥工业大学））；School of Cyber Science and Technology, Beihang University（网络科学与技术学院，北航）；Department of Bioinformatics, Hangzhou Nuowei Information Technology Co.,Ltd（生物信息学系，杭州诺威信息技术有限公司）
BIFOLD & TU Berlin（BIFOLD与柏林工业大学）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft Research（微软研究院）；The University of Melbourne（墨尔本大学）；University of Toronto & Vector Institute（多伦多大学与向量研究所）
Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
Galician Research and Development Center in Advanced Telecommunications (GRADIANT)（加利西亚先进电信研究与发展中心（GRADIANT））；atlanTTic, Information & Computing Lab (ICLAB), Telecommunication Engineering School, Universidade de Vigo（atlanTTic，信息与计算实验室（ICLAB），电信工程学校，维戈大学）
FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning
MBZUAI；Syllogia；The University of Tokyo（东京大学）；The Fin AI（Fin AI）；Cornell University（康奈尔大学）；The University of Melbourne（墨尔本大学）；IIT Delhi（德里理工学院）；The University of Manchester（曼彻斯特大学）
