Differential Privacy for Dummies
A beginner-friendly introduction to understanding differential privacy and its applications.
What is Differential Privacy?
Differential privacy is a mathematical framework that provides formal guarantees about the privacy of individuals in a dataset, even when statistical queries are made against that dataset.
Key Concepts
The Privacy Parameter (ε)
The epsilon (ε) parameter controls the privacy-utility tradeoff:
- Lower ε = More privacy, less accuracy
- Higher ε = Less privacy, more accuracy
Randomized Response
One of the simplest differential privacy mechanisms. Add controlled noise to responses to protect individual privacy.
Applications
- Census data collection
- Machine learning model training
- Healthcare analytics
- Location data aggregation
Further Reading
Coming soon.