Verifiable evaluations of machine learning models using zkSNARKs (2402.02675v2)

Abstract: In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results-whether over task accuracy, bias evaluations, or safety checks-are traditionally impossible to verify by a model end-user without the costly or impossible process of re-performing the benchmark on black-box model outputs. This work presents a method of verifiable model evaluation using model inference through zkSNARKs. The resulting zero-knowledge computational proofs of model outputs over datasets can be packaged into verifiable evaluation attestations showing that models with fixed private weights achieve stated performance or fairness metrics over public inputs. We present a flexible proving system that enables verifiable attestations to be performed on any standard neural network model with varying compute requirements. For the first time, we demonstrate this across a sample of real-world models and highlight key challenges and design solutions. This presents a new transparency paradigm in the verifiable evaluation of private models.

• The paper presents a novel zkSNARKs framework that generates succinct proofs to verify machine learning model metrics.
• It employs challenge-based verification to confirm performance and fairness without exposing the model's private weights.
• The study analyzes computational costs and incentive models, demonstrating practical viability for secure, transparent AI evaluation.

Enhancing Transparency in AI with zkSNARKs-based Model Evaluations

Introduction to Verifiable Evaluation Attestations

The advent of proprietary machine learning models has accentuated the challenge of scrutinizing these models' accuracy, bias, and safety measures due to the opaqueness surrounding their internal workings and data handling. Traditional reliance on the evaluation metrics reported by model developers raises concerns about the authenticity of these claims, especially when such models influence critical decisions in healthcare, law enforcement, and commercial applications. Addressing these challenges requires a paradigm shift towards more transparent, verifiable model evaluations. This paper introduces a novel approach leveraging zkSNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) for the verifiable evaluation of machine learning models. This method permits the creation of computational proofs for model outputs over datasets, thus facilitating the verification of performance or fairness metrics without revealing the model's private weights.

Background and Significance

Current practices around machine learning model deployment and evaluation often lack the necessary transparency, making it difficult to verify the claims made by model developers regarding performance metrics like accuracy or fairness. This opacity is particularly problematic in an era where AI's ethical implications and societal impacts are scrutinized. It creates a trust deficit between model providers and users, which can hamper the adoption of AI technologies in sensitive and high-stakes domains. To counteract this, the method proposed in this paper builds upon existing literature on secure inference and zkSNARKs to offer a robust framework for the verifiable evaluation of neural networks and other model types.

The Proposed Framework

The framework outlined in the paper facilitates the generation of verifiable evaluation attestations for a wide range of model architectures and tasks. It can be distilled into the following components:

• General-purpose Verification: A versatile architecture for verifying neural networks via succinct non-interactive proofs that span various tasks and datasets, making it broadly applicable.
• Model Inference Verification: A challenge-based mechanism to confirm that model inferences match the performance metrics of the attested model weights, providing a direct link between claimed and actual model performance.
• Incentive Models and Computational Costs: An analysis of the intrinsic and extrinsic costs associated with model evaluation, crafting realistic incentive models for adopting verifiable evaluations in practice.
• Real-world Applicability: The demonstration of the methodology across diverse real-world models underscores its practical viability and addresses the critical design challenges inherent in such an endeavor.

Implications and Future Directions

The realization of a transparent evaluation mechanism for private models presents a significant leap towards addressing trust and verification issues in machine learning applications. By enabling end-users to validate model claims independently, this approach can foster greater accountability and ethical compliance in AI deployment. Moreover, the potential to extend these verifiable evaluations to more complex model architectures and larger datasets paves the way for widespread adoption across various AI domains.

Looking ahead, further optimization of computational resources and proof generation processes remains an area ripe for exploration. Enhancements focusing on reducing the overhead associated with generating zkSNARKs could make verifiable evaluations more accessible and economically feasible for a broader array of applications. Additionally, extending this framework to accommodate emerging model types and novel machine learning paradigms will be crucial for maintaining its relevance and utility in the fast-evolving field of AI.

Conclusion

The framework for verifiable model evaluations using zkSNARKs introduced in this paper represents a pivotal step towards enhanced transparency and accountability in machine learning. By bridging the gap between proprietary model operations and the need for independent verification, this approach has the potential to reshape the landscape of AI trustworthiness. As the field continues to advance, the ongoing development of methods for efficient, scalable, and secure model evaluations will remain a central pillar supporting the ethical and responsible use of AI technologies.