Digital-privacy

Differential privacy limits what an analysis reveals about an individual contribution. Learn how calibrated noise, privacy budgets, and careful system design make useful statistics possible without publishing exact personal records.

Retrieval-augmented generation gives an AI model relevant documents before it answers. Here is how the process works, how it differs from training, and why better evidence does not guarantee a correct answer.

Confidential computing protects sensitive code and data while a processor is actively using them. Here is how trusted execution environments work, what attestation proves, and where the protection ends.

Federated learning lets many devices or organizations improve a shared AI model without pooling their raw data. Here is how the training loop works, what it protects, and where its limits begin.

On-device AI moves useful machine learning tasks from distant servers to the phone, laptop, or wearable in your hand. Here is how it works, where it helps, and what its limits mean for you.