Differential privacy composition theorems are mathematical tools used in

differential privacy Differential privacy (DP) is a mathematically rigorous framework for releasing statistical information about datasets while protecting the privacy of individual data subjects. It enables a data holder to share aggregate patterns of the group while ...

to analyze and bound the accumulated

privacy Privacy (, ) is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively. The domain of privacy partially overlaps with security, which can include the concepts of a ...

loss when multiple differentially private mechanisms are applied to the same dataset. They quantify how privacy guarantees degrade as more queries or analyses are performed, and are essential for designing complex differentially private systems and algorithms. For example, if user submits multiple queries to a differentially private database, each query might individually satisfies ε-differential privacy but the repeated interaction can cumulatively leak more information than intended. Composition theorems address this by providing a way to calculate the overall privacy loss after multiple mechanisms have been applied.

References

Differential privacy Theory of cryptography {{Comp-sci-stub