Congenial Differential Privacy under Mandated Disclosure
This work addresses the challenge of producing transparent and statistically valid differentially private data under legal and ethical constraints, which is incremental as it builds on existing theory of congeniality in multi-phase processing.
The paper tackles the problem of ensuring that differentially private data releases satisfy external constraints while maintaining statistical validity, by integrating mandated disclosure directly into the privacy mechanism through probabilistic conditioning, which eliminates the need for extra post-processing phases. It provides initial theoretical guarantees and a Markov chain algorithm for this approach.
Differentially private data releases are often required to satisfy a set of external constraints that reflect the legal, ethical, and logical mandates to which the data curator is obligated. The enforcement of constraints, when treated as post-processing, adds an extra phase in the production of privatized data. It is well understood in the theory of multi-phase processing that congeniality, a form of procedural compatibility between phases, is a prerequisite for the end users to straightforwardly obtain statistically valid results. Congenial differential privacy is theoretically principled, which facilitates transparency and intelligibility of the mechanism that would otherwise be undermined by ad-hoc post-processing procedures. We advocate for the systematic integration of mandated disclosure into the design of the privacy mechanism via standard probabilistic conditioning on the invariant margins. Conditioning automatically renders congeniality because any extra post-processing phase becomes unnecessary. We provide both initial theoretical guarantees and a Markov chain algorithm for our proposal. We also discuss intriguing theoretical issues that arise in comparing congenital differential privacy and optimization-based post-processing, as well as directions for further research.