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2024

Privacy-Preserving Decentralized AI with Confidential Computing

Lee, Dayeol, António, Jorge, Khan, Hisham

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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.

Privacy-Preserving Decentralized AI with Confidential Computing · Around

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