Episode 47 — Reduce Model Inversion and Leakage: Privacy Attacks and Practical Mitigations (Domain 3)
Model inversion and membership inference attacks are privacy-focused threats where an attacker attempts to extract sensitive training data or determine if a specific individual's data was used in the model. This episode details these "leakage" risks, which are particularly dangerous when models are trained on PII or proprietary information. For the AAIR exam, candidates must know how to apply mitigations such as differential privacy, which adds controlled noise to the data or model gradients to mask individual contributions. We also discuss the risk of "over-memorization," where a model becomes a database of its training samples rather than a generalizer. Practical controls include limiting the precision of the model's confidence scores in its output, as high-precision scores can often be used to reverse-engineer training features. By understanding these privacy-enhancing technologies, risk managers can deploy AI models that provide utility without compromising the fundamental privacy rights of the individuals whose data made the model possible. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
