Differential privacy (DP) is a mathematical framework designed to enable private data analysis while providing strong guarantees about the privacy of individuals in a dataset. It ensures that the output of a query does not reveal sensitive information about any single person, even if an adversary has access to all other data.

Key Concepts

  • Privacy Budget (ε): A parameter that quantifies the maximum allowable information leakage. Lower ε means stronger privacy.
  • Noise Addition: Random noise (e.g., Laplace or Gaussian) is injected into results to mask individual contributions.
  • Composition Theorem: Limits on privacy loss accumulate over multiple queries, ensuring cumulative privacy protection.

Core Mechanisms

  1. Laplace Mechanism
    Adds noise proportional to the sensitivity of the function.

    Laplace Mechanism

  2. Randomized Response Technique
    Uses probabilistic responses to protect participant anonymity in surveys.

    Randomized Response

  3. Exponential Mechanism
    Selects outputs based on a utility function while preserving privacy.

    Exponential Mechanism

Applications

  • Healthcare data analysis
  • Census surveys
  • Machine learning training
  • Location tracking

For deeper exploration, check our Privacy Preservation Tutorial to understand related techniques. 📚💡

Data Anonymization

Resources

This tutorial provides a foundational understanding of differential privacy principles and their implementation. 🌐🔒