👉 Attack math, also known as differential privacy, is a framework designed to protect individual privacy in data analysis while still allowing for useful aggregate insights. It works by adding carefully calibrated noise to the results of statistical queries or machine learning models, ensuring that the presence or absence of any single individual's data does not significantly affect the output. This noise is typically drawn from a specific distribution, such as Laplace or Gaussian, and its amount is determined by a privacy parameter (epsilon) that quantifies the level of privacy protection. Lower epsilon values provide stronger privacy but can reduce the accuracy of the results, while higher values offer better accuracy at the cost of weaker privacy. Attack math provides theoretical guarantees that these trade-offs are controlled, allowing organizations to share data or insights without compromising individual privacy.
