# Issues Encountered Deploying Differential Privacy

arXiv: https://arxiv.org/abs/1809.02201 · HTML: https://mlsanity.com/paper/1809.02201/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [A Fast Algorithm for Adaptive Private Mean Estimation](https://mlsanity.com/paper/2301.07078/) (2023)
- [Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions](https://mlsanity.com/paper/2309.08678/) (2023)
- [Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation method](https://mlsanity.com/paper/2310.06571/) (2023)
- [Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government](https://mlsanity.com/paper/2410.16423/) (2024)
- [Private and interpretable clinical prediction with quantum-inspired tensor train models](https://mlsanity.com/paper/2602.06110/) (2026)

## Abstract (arXiv)

When differential privacy was created more than a decade ago, the motivating example was statistics published by an official statistics agency. In attempting to transition differential privacy from the academy to practice, the U.S. Census Bureau has encountered many challenges unanticipated by differential privacy's creators. These challenges include obtaining qualified personnel and a suitable computing environment, the difficulty accounting for all uses of the confidential data, the lack of release mechanisms that align with the needs of data users, the expectation on the part of data users that they will have access to micro-data, and the difficulty in setting the value of the privacy-loss parameter, ε (epsilon), and the lack of tools and trained individuals to verify the correctness of differential privacy implementations.