What is differential privacy? Differential privacy is a rigorous mathematical framework that allows organizations to share aggregate statistical insights from a dataset while mathematically bounding the risk of identifying any individual record. It works by injecting calibrated mathematical noise (e.g., Laplace or Gaussian mechanisms) into analytical query results.
| Parameter | Technical Role | Trade-Off Balance |
|---|---|---|
| Epsilon (ε) | The Privacy Budget: bounds the maximum impact one record has on query results | Smaller ε = stronger privacy + more noise; Larger ε = higher accuracy + less privacy |
| Delta (δ) | Failure Probability: the small probability that the epsilon bound may fail | Typically set strictly smaller than 1/N (N = dataset size) |
| Noise Mechanism | Laplace, Gaussian, or Exponential probability distributions | Calibrated precisely to query sensitivity |
In modern AI, differential privacy is integrated into training pipelines (DP-SGD) to prevent neural networks from memorizing private training data, providing robust mathematical defense against model inversion attacks.
What Glossary Professionals Send to AI — and What They Should Be Sending Instead
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