Privacy-Preserving Decentralized AI with Confidential Computing
This work addresses privacy protection for decentralized AI platforms in the Web3 domain, offering a solution to secure sensitive assets in untrusted environments, though it appears incremental by integrating existing TEE technology.
The paper tackles privacy challenges in decentralized AI by proposing the use of Confidential Computing with Trusted Execution Environments (TEEs) to secure model parameters and user data, addressing the high computational overhead of cryptography-based methods like zero-knowledge machine learning.
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.