# Privacy-Preserving Decentralized AI with Confidential Computing

arXiv: https://arxiv.org/abs/2410.13752 · HTML: https://mlsanity.com/paper/2410.13752/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Heckler: Breaking Confidential VMs with Malicious Interrupts](https://mlsanity.com/paper/2404.03387/) (2024)
- [WeSee: Using Malicious #VC Interrupts to Break AMD SEV-SNP](https://mlsanity.com/paper/2404.03526/) (2024)

## Built on by (most cited first, 3 of 3)

- [C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System](https://mlsanity.com/paper/2412.13163/) (2024)
- [Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchains](https://mlsanity.com/paper/2506.04236/) (2025)
- [Is Decentralized AI Governable? From Regulative Policy to Constitutive Protocol](https://mlsanity.com/paper/2605.24538/) (2026)

## Abstract (arXiv)

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.