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3 posts

• Differential Privacy in Practice: What the Math Guarantees (and What It Doesn't) for AI Training Data

  Differential privacy is the strongest mathematical privacy guarantee for ML training data — but the gap between formal ε-DP and deployed reality means many 'private' AI systems aren't nearly as private as advertised. This post bridges the math and the practice.

  Jul 6, 2026 privacy differential-privacy dp-sgd membership-inference training-data epsilon attack-defense llm-security

• Membership Inference Attacks: Detecting What Was in an AI Model's Training Data

  Membership inference attacks let adversaries determine—with significant accuracy—whether a specific data record was used to train a model. This post covers the attack mechanisms, why models leak this information, real-world threat scenarios, and how differential privacy and machine unlearning address (and fail to fully close) the gap.

  Jul 5, 2026 privacy membership-inference differential-privacy machine-unlearning attack-defense gdpr overfitting

• Machine Unlearning Security: When Forgetting Training Data Creates New Vulnerabilities

  Regulators are demanding AI systems forget specific training data. Attackers are figuring out how to weaponize that forgetting — through verification gaps, induced forgetting abuse, and unlearning as a path to alignment degradation.

  Jul 2, 2026 machine-unlearning privacy gdpr safety-alignment adversarial-ml model-security membership-inference