Enabling Trustless Audits of Machine Learning Models without Revealing Sensitive Data or Model Weights

Core Concepts

It is possible to simultaneously allow model providers to keep their model weights and data secret while allowing other parties to trustlessly audit model and data properties through the use of zero-knowledge proofs.

Abstract

The paper presents a protocol called ZKAUDIT that enables trustless audits of machine learning models without revealing the underlying data or model weights. The key idea is that the model provider publishes cryptographic commitments of the dataset and model weights, alongside a zero-knowledge proof certifying that the published commitments are derived from training the model. The model provider can then respond to audit requests by privately computing any function of the dataset or model and releasing the output alongside another zero-knowledge proof certifying the correct execution of the function.
To enable ZKAUDIT, the authors develop new methods of computing zero-knowledge proofs for stochastic gradient descent on modern neural networks, including techniques for high-performance softmax computation and fixed-point arithmetic. They show that ZKAUDIT can provide trustless audits of deep neural networks, including copyright, censorship, and counterfactual audits, with little to no loss in accuracy. The cost of auditing a recommender system and image classification system can be as low as $10 and $108, respectively, demonstrating the practicality of their approach.
The paper first provides background on zero-knowledge proofs and how they can be used to represent computations. It then describes the ZKAUDIT protocol in detail, including the two main steps: ZKAUDIT-T for proving the training process, and ZKAUDIT-I for executing arbitrary audit functions. The authors analyze the security of ZKAUDIT and discuss its limitations.
The bulk of the paper focuses on the technical challenges of computing zero-knowledge proofs for gradient descent, including the need for rounded division and variable-precision fixed-point arithmetic. The authors present extensive evaluations of the performance and accuracy of their techniques, comparing to prior work and demonstrating the feasibility of their approach on real-world datasets.
Finally, the paper explores several example audits that can be performed using ZKAUDIT, such as censorship detection, counterfactual analysis, copyright verification, and demographic disparity checks. The authors show that these audits can be performed at reasonable cost, highlighting the practical utility of their work.

Stats

The training dataset for the image classification tasks contains 15,557 images for dermnet, 1,020 images for flowers-102, and 8,144 images for cars.
The movielens dataset for the recommender system has 6,040 users, 3,706 movies, and 900,188 ratings.

Quotes

"There is an increasing conflict between business incentives to hide models and data as trade secrets, and the societal need for algorithmic transparency."
"Finding a mutually agreeable third party is difficult, and the associated costs often make this approach impractical."
"ZKAUDIT, via zero-knowledge proofs, allows a model provider to selectively reveal properties of the training data and model without a trusted third party such that any party can verify the proof after the fact (i.e., the audit is non-interactive)."

Key Insights Distilled From

Trustless Audits without Revealing Data or Models

by Suppakit Wai... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04500.pdf

Deeper Inquiries

How could ZKAUDIT be extended to handle data poisoning attacks or protect the model architecture as well as the weights?

ZKAUDIT could be extended to handle data poisoning attacks by incorporating mechanisms to detect and mitigate the effects of such attacks during the auditing process. This could involve introducing additional checks and validations to ensure that the training data has not been manipulated or poisoned to influence the model's behavior. Techniques such as outlier detection, anomaly detection, and data integrity checks could be implemented within the ZKAUDIT framework to identify and address data poisoning attempts.
To protect the model architecture as well as the weights, ZKAUDIT could utilize advanced cryptographic techniques to secure both the model structure and the trained parameters. By incorporating encryption methods and secure multiparty computation protocols, ZKAUDIT could ensure that the model architecture remains confidential and the model weights are protected from unauthorized access or tampering. This would enhance the overall security and privacy of the auditing process, safeguarding sensitive information from malicious actors.

How could the ideas behind ZKAUDIT be applied to other domains beyond machine learning, such as verifying the execution of complex algorithms or computations in a privacy-preserving manner?

The concepts and principles behind ZKAUDIT can be applied to various domains beyond machine learning to verify the execution of complex algorithms or computations in a privacy-preserving manner. For instance, in the financial sector, ZKAUDIT could be used to audit the execution of trading algorithms while keeping the proprietary trading strategies confidential. By leveraging zero-knowledge proofs and cryptographic techniques, financial institutions can demonstrate the validity of their trading algorithms without revealing sensitive information.
In the healthcare industry, ZKAUDIT could be utilized to verify the execution of medical algorithms or protocols while maintaining patient privacy. Healthcare providers could prove compliance with regulations and standards without disclosing patient data by employing privacy-preserving auditing mechanisms similar to ZKAUDIT. This would ensure the integrity and accuracy of medical processes while upholding patient confidentiality.
Overall, the principles of ZKAUDIT can be adapted to various sectors where the verification of complex algorithms or computations is required, enabling organizations to demonstrate compliance, accuracy, and trustworthiness without compromising sensitive information.

What are the potential limitations or drawbacks of using zero-knowledge proofs for auditing machine learning models in practice?

While zero-knowledge proofs offer significant advantages for auditing machine learning models, there are potential limitations and drawbacks to consider in practice:

Computational Complexity: Zero-knowledge proofs can be computationally intensive, leading to high processing and verification times. This could result in delays in the auditing process, especially for large-scale models or datasets.

Scalability Challenges: Auditing complex machine learning models with zero-knowledge proofs may face scalability challenges, particularly when dealing with deep neural networks or extensive training data. Ensuring efficient and scalable implementation is crucial for practical applications.

Proof Generation Costs: Generating zero-knowledge proofs can be resource-intensive and costly, especially when using advanced cryptographic techniques. The expenses associated with proof generation and verification could be prohibitive for some organizations.

Complexity of Implementation: Implementing zero-knowledge proofs for auditing ML models requires expertise in cryptography and secure computation. Organizations may face challenges in integrating these techniques into existing workflows and systems.

Limited Interpretability: Zero-knowledge proofs focus on verifying the correctness of computations without revealing the underlying data or algorithms. This lack of transparency could hinder the interpretability of audit results and make it challenging to understand the reasoning behind certain outcomes.

Addressing these limitations through optimization, efficient algorithms, and specialized tools is essential to maximize the benefits of using zero-knowledge proofs for auditing machine learning models in practice.