How do you address data leakage issues when training generative models on confidential data

Question

With the help of code snippets, can you tell me How do you address data leakage issues when training generative models on confidential data?

Answer 1

You can address data leakage issues when training generative models on confidential data, by referring to the following:

• Data Splitting: Use strict train-test-validation splits to ensure no overlap between sets.
• Differential Privacy: Incorporate techniques like noise addition to protect sensitive data.
• Federated Learning: Train models across decentralized data sources without sharing raw data.
• Synthetic Data Validation: Ensure generated data does not directly replicate training samples using similarity checks.
• Access Control: Restrict access to the training data and logs containing sensitive information.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Differential Privacy: Adds noise to gradients to prevent leakage of individual data points.
• Federated Learning: Distributes training without centralized data aggregation.
• Strict Splitting: Enforces non-overlapping train-test sets.
• Synthetic Data Validation: Implements tests to ensure no overfitting or direct replication of training samples.

Hence, by referring to above, you can address data leakage issues when training generative models on confidential data.