Privacy in ML

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Protecting user privacy in ML systems. Federated learning (train on device, aggregate gradients), differential privacy (add noise to guarantee privacy), membership inference attacks (detect if a sample was in training data).

Related

• Synthetic Data (privacy-preserving alternative)
• AI Safety (privacy as safety concern)

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