Definition of Privacy Parameters (ε and δ) and their importance
1. ε-Differential Privacy: The parameter ε (epsilon) is a non-negative value that quantifies the privacy loss associated with a specific computation. A mechanism is said to achieve ε-differential privacy if the probability of obtaining any output is approximately the same regardless of whether an individual’s data is included in the dataset or not. A smaller ε indicates stronger privacy, implying that the impact of any individual’s data on the analysis result is minimal.
2. δ-Differential Privacy: The parameter δ (delta) is another parameter introduced to address the possibility of privacy breaches due to rare events. It is used primarily in scenarios where the privacy guarantee might be slightly relaxed for extremely small probabilities. A mechanism achieves δ-differential privacy if the maximum allowable privacy loss is bounded by δ.
While ε provides a clear measure of how differentially private an analysis is, δ allows for fine-tuning the privacy guarantee to accommodate rare situations while maintaining a strong level of privacy overall.