Towards Secure and Private AI: A Framework for Decentralized Inference

TL;DR

This paper proposes a comprehensive framework for secure and private decentralized AI inference, integrating zero-knowledge proofs, consensus checks, split learning, and TEEs to enhance trust, privacy, and reliability in multimodal AI systems.

Contribution

It introduces a novel framework combining multiple security and privacy techniques specifically designed for decentralized multimodal AI inference systems.

Findings

Framework effectively enhances security and privacy in decentralized AI

Consensus checks improve model reliability and reduce hallucinations

Zero-knowledge proofs enable secure model verification without data exposure

Abstract

The rapid advancement of ML models in critical sectors such as healthcare, finance, and security has intensified the need for robust data security, model integrity, and reliable outputs. Large multimodal foundational models, while crucial for complex tasks, present challenges in scalability, reliability, and potential misuse. Decentralized systems offer a solution by distributing workload and mitigating central points of failure, but they introduce risks of unauthorized access to sensitive data across nodes. We address these challenges with a comprehensive framework designed for responsible AI development. Our approach incorporates: 1) Zero-knowledge proofs for secure model verification, enhancing trust without compromising privacy. 2) Consensus-based verification checks to ensure consistent outputs across nodes, mitigating hallucinations and maintaining model integrity. 3) Split Learning techniques that segment models across different nodes, preserving data privacy by preventing full data access at any point. 4) Hardware-based security through trusted execution environments (TEEs) to protect data and computations. This framework aims to enhance security and privacy and improve the reliability and fairness of multimodal AI systems. Promoting efficient resource utilization contributes to more sustainable AI development. Our state-of-the-art proofs and principles demonstrate the framework's effectiveness in responsibly democratizing artificial intelligence, offering a promising approach for building secure and private foundational models.