用可信执行环境保护去中心化AI中的模型和数据隐私
Privacy-Preserving Decentralized AI with Confidential Computing
- 用硬件级可信执行环境隔离敏感计算，防止数据泄露
- 在Atoma网络中实现去中心化AI的隐私保护，避免传统加密方案的高开销
- 适合关注Web3领域隐私安全的开发者与研究者
本文针对去中心化人工智能（AI）中的隐私保护问题，提出在Atoma网络——一个面向Web3领域的去中心化AI平台——中引入可信计算（Confidential Computing, CC）。去中心化AI通过多实体协作提供服务，虽提升透明性与鲁棒性，但存在模型参数和用户数据暴露于不可信参与方的风险。基于密码学的隐私技术如零知识机器学习（zkML）存在计算开销过大问题。为此，本文探索利用硬件级可信执行环境（Trusted Execution Environments, TEEs）实现敏感数据的隔离处理，确保即使在潜在不信任环境中，模型参数与用户数据仍保持安全。尽管TEEs存在若干局限，但其有望弥合去中心化AI中的隐私差距。本文进一步研究了如何将TEEs集成至Atoma的去中心化架构中。
原文摘要 · Abstract (English)
This paper addresses privacy protection in decentralized Artificial Intelligence (AI) using Confidential Computing (CC) within the Atoma Network, a decentralized AI platform designed for the Web3 domain. Decentralized AI distributes AI services among multiple entities without centralized oversight, fostering transparency and robustness. However, this structure introduces significant privacy challenges, as sensitive assets such as proprietary models and personal data may be exposed to untrusted participants. Cryptography-based privacy protection techniques such as zero-knowledge machine learning (zkML) suffers prohibitive computational overhead. To address the limitation, we propose leveraging Confidential Computing (CC). Confidential Computing leverages hardware-based Trusted Execution Environments (TEEs) to provide isolation for processing sensitive data, ensuring that both model parameters and user data remain secure, even in decentralized, potentially untrusted environments. While TEEs face a few limitations, we believe they can bridge the privacy gap in decentralized AI. We explore how we can integrate TEEs into Atoma's decentralized framework.
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