Differential Privacy

Also known as: Differential Privacy, DP, ε-Differential Privacy

Mathematical technique adding calibrated noise to data to protect individual privacy while preserving the dataset's statistical utility.

Differential Privacy is a rigorous mathematical definition of privacy and associated techniques to achieve it. It guarantees that including or excluding any individual in a dataset has a minimal and controlled impact on the results of any analysis, making it impossible to infer specific information about any individual from aggregate results.

Technically implemented by adding random calibrated noise (typically Gaussian or Laplace noise) to database queries. The epsilon (ε) parameter controls the privacy-utility trade-off (small ε = more noise = more privacy).

Apple, Google, and the US Census Bureau use differential privacy in production. In market research, it is especially relevant for analysis of sensitive behavioral data (health, finance) and for sharing research data between organizations while maintaining respondent protection.

Atlantia applies differential privacy principles in respondent data handling to comply with LATAM data protection regulations.