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.