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Is the AI model suited for processing confidential information?
Is the AI model suited for processing confidential information?
- There are certain kinds of machine learning (ML) models which actually contain parts of the training data in its raw form within them by design. For example, ‘support vector machines’ (SVMs) and ‘k-nearest neighbours’ (KNN) models contain some of the training data in the model itself.
- Algorithmic leakage is an issue that should be considered carefully. Source: BerryVilleiML
If you answered No then you are at risk
If you are not sure, then you might be at risk too
Recommendations
When selecting the algorithm, conduct a thorough analysis to evaluate the risk of algorithmic leakage. For models known to retain training data (e.g., k-nearest neighbors, support vector machines), assess whether sensitive or identifiable information could be exposed through predictions or reverse engineering.
- Perform privacy risk assessments and adversarial testing to detect memorization or data leakage.
- Use privacy-preserving techniques where appropriate (e.g., differential privacy, data minimization, feature abstraction).
- Avoid using algorithms prone to leakage when working with sensitive data, or take extra steps to anonymize and sanitize training inputs.
- Include leakage testing in your model evaluation pipeline, especially for high-risk or regulated domains.