为什么值得关注

它提供了可验证/隐私 AI 领域最清晰的结构化分类，结合了 2x2 框架与每个方法及每篇论文的详细参考，并包含构建该领域的组织级别的景观地图。

适合谁使用

密码学研究人员与工程师
寻求可验证或隐私推理的机器学习从业者
去中心化 AI 项目团队
评估 AI 可信度的审计人员与安全专家

典型使用场景

为特定 AI 阶段选择正确的密码学方法（如 ZK、MPC、FHE）
理解 AI 系统中可验证性与隐私性之间的权衡
识别专注于每种技术的关键公司与研究团队
全面了解可信 AI 中的开放问题与成本结构

项目优势

涵盖推理/训练与可验证性/隐私性的全面 2x2 分类
链接到生成式网站，每个方法和每篇论文都有独立页面，数据附带来源追踪
包含公司及研究团队的景观地图，区分真正的 zkML 初创公司与邻近项目
讨论了成本权衡（如可验证性带来约 1000 倍计算开销）及信任模型

使用前须知

并非工具或库，而是经过策划的知识库与参考资料
随着领域快速演变，景观地图可能过时
主要聚焦密码学技术；非密码学方法（如激励机制、TEE）被标记为邻近领域，未深度覆盖

README 快速开始

zkAI: Verifiable and Private AI

A systematization of knowledge on making AI trustworthy, from zkSecurity. The "zk" is historical: zero-knowledge proofs are one technique here, not the scope.

The full SoK is a generated site, one page per approach and one per paper, with every number rendered from the data and carrying its provenance.

The map

The field is a 2×2: what phase of the model's life you protect, and what property you want. Each cell is served by several cryptographic (and non-cryptographic) approaches.

	Verifiability (prove it was done right)	Privacy (hide the data/model during compute)
Inference	Proving inference: zkML. DeepProve, Jolt Atlas, zkPyTorch, zkGPT, zkLLM.	Private inference: 2PC/MPC/FHE. Iron, BOLT, CipherGPT, Nimbus.
Training	Proving training: zkPoT. Kaizen, zkDL, Optimum Vicinity, ZKBoost.	Private training: MPC/HE. PriFT, and verifiable-FL hybrids.

The technique is not the axis. A cell can be filled by ZK proofs, MPC, FHE, trusted hardware (TEEs), optimistic fraud proofs, or trace sampling, at very different cost/trust trade-offs. Verifiability buys correctness (a third party can trust the result) at ~1000× compute. Privacy buys confidentiality from a counterparty (usually only a semi-honest one) at the price of heavy communication or homomorphic compute. They are different guarantees, and most systems give one, not both.

Within the verifiability column, the ZKP-VML survey splits what is being proven three ways, and we add a fourth.

Objective	Claim being proven	Cost
Inference	"This model produced this output on this input."	Cheapest
Testing	"This model achieves this accuracy on this dataset."	Middle
Training	"This model is the result of correctly training on this data."	Hardest

项目描述

using agents to monitor proving training and inference techniques

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