Congenial Differential Privacy under Mandated Disclosure

Ruobin Gong, Xiao Li Meng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationFODS 2020 - Proceedings of the 2020 ACM-IMS Foundations of Data Science Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages12
ISBN (Electronic)9781450381031
StatePublished - Oct 19 2020
Event2020 ACM-IMS Foundations of Data Science Conference, FODS 2020 - Virtual, Online, United States
Duration: Oct 19 2020Oct 20 2020

Publication series

NameFODS 2020 - Proceedings of the 2020 ACM-IMS Foundations of Data Science Conference


Conference2020 ACM-IMS Foundations of Data Science Conference, FODS 2020
Country/TerritoryUnited States
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems


  • belief function
  • conditioning
  • invariants
  • monte carlo
  • post-processing
  • statistical intelligibility
  • uncongeniality


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