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.