Privacy-aware identification

Understand

The paper redefines econometric identification under formal privacy constraints, particularly differential privacy (DP).

• Traditionally, econometrics focuses on point or partial identification, aiming to recover parameters precisely or within a deterministic set.
• However, DP introduces a fundamental challenge: information asymmetry between researchers and data curators results in DP outputs belonging to a potentially large collection of differentially private statistics, which is naturally described as a random set.
• Due to the finite-sample nature of the DP notion and mechanisms, identification must be reinterpreted as the ability to recover parameters in the limit of this random set.