# Distribution-Invariant Differential Privacy

arXiv: https://arxiv.org/abs/2111.05791 · HTML: https://mlsanity.com/paper/2111.05791/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [cpSGD: Communication-efficient and differentially-private distributed SGD](https://mlsanity.com/paper/1805.10559/) (2018)
- [Collecting Telemetry Data Privately](https://mlsanity.com/paper/1712.01524/) (2017)
- [Minimax Optimal Procedures for Locally Private Estimation](https://mlsanity.com/paper/1604.02390/) (2016)
- [Practical Differentially Private Top-k Selection with Pay-what-you-get Composition](https://mlsanity.com/paper/1905.04273/) (2019)
- [Towards Formalizing the GDPR's Notion of Singling Out](https://mlsanity.com/paper/1904.06009/) (2019)
- [Privacy-preserving parametric inference: a case for robust statistics](https://mlsanity.com/paper/1911.10167/) (2019)
- [Comparative Study of Differentially Private Data Synthesis Methods](https://mlsanity.com/paper/1602.01063/) (2016)
- [PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn](https://mlsanity.com/paper/1809.07754/) (2018)
- [Congenial Differential Privacy under Mandated Disclosure](https://mlsanity.com/paper/2008.10202/) (2020)

## Built on by (most cited first, 6 of 6)

- [Utility Theory of Synthetic Data Generation](https://mlsanity.com/paper/2305.10015/) (2023)
- [Boosting Data Analytics With Synthetic Volume Expansion](https://mlsanity.com/paper/2310.17848/) (2023)
- [Privacy-Preserving Community Detection for Locally Distributed Multiple Networks](https://mlsanity.com/paper/2306.15709/) (2023)
- [Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification](https://mlsanity.com/paper/2305.18671/) (2023)
- [Rate-Optimal Rank Aggregation with Private Pairwise Rankings](https://mlsanity.com/paper/2402.16792/) (2024)
- [Differentially Private Truncation of Unbounded Data via Public Second Moments](https://mlsanity.com/paper/2602.22282/) (2026)

## Abstract (arXiv)

Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public health, information technology, and the U.S. decennial census. Nevertheless, to guarantee differential privacy, existing methods may unavoidably alter the conclusion of the original data analysis, as privatization often changes the sample distribution. This phenomenon is known as the trade-off between privacy protection and statistical accuracy. In this work, we mitigate this trade-off by developing a distribution-invariant privatization (DIP) method to reconcile both high statistical accuracy and strict differential privacy. As a result, any downstream statistical or machine learning task yields essentially the same conclusion as if one used the original data. Numerically, under the same strictness of privacy protection, DIP achieves superior statistical accuracy in a wide range of simulation studies and real-world benchmarks.