Authors: Bianca-Mihaela Ganescu, Jonathan Passerat-Palmbach

Published on: February 09, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.06414

Summary

• What is new: Introduction of Zero-Knowledge Machine Learning (ZKML) and its application with transformers through snarkGPT to ensure fairness and privacy.
• Why this is important: Concerns over fairness, transparency, and reliability in AI models, particularly in sensitive fields like medicine and law.
• What the research proposes: Using Zero-Knowledge Proofs (ZKPs) with Machine Learning models to enable the validation of AI outputs without exposing the model’s sensitive information.
• Results: Evidence that snarkGPT is scalable and can impartially assess AI performance while protecting model privacy.

Technical Details

Technological frameworks used: Zero-Knowledge Proofs (ZKPs), ZKML

Models used: Transformers

Data used: nan

Potential Impact

Healthcare and legal sectors, AI development and auditing firms, privacy-conscious service industries

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