# Marginal Release Under Local Differential Privacy

arXiv: https://arxiv.org/abs/1711.02952 · HTML: https://mlsanity.com/paper/1711.02952/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2017.

## Built on by (most cited first, 9 of 9)

- [Differential Privacy for Text Analytics via Natural Text Sanitization](https://mlsanity.com/paper/2106.01221/) (2021)
- [Differentially Private Query Release Through Adaptive Projection](https://mlsanity.com/paper/2103.06641/) (2021)
- [Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation](https://mlsanity.com/paper/2111.02356/) (2021)
- [Tabular Data Synthesis with Differential Privacy: A Survey](https://mlsanity.com/paper/2411.03351/) (2024)
- [Differential Privacy with Random Projections and Sign Random Projections](https://mlsanity.com/paper/2306.01751/) (2023)
- [Locally Differentially Private Bayesian Inference](https://mlsanity.com/paper/2110.14426/) (2021)
- [Bayesian Frequency Estimation Under Local Differential Privacy With an Adaptive Randomized Response Mechanism](https://mlsanity.com/paper/2405.07020/) (2024)
- [Perturb-and-Project: Differentially Private Similarities and Marginals](https://mlsanity.com/paper/2406.04868/) (2024)
- [Training with Differential Privacy: A Gradient-Preserving Noise Reduction Approach with Provable Security](https://mlsanity.com/paper/2409.11663/) (2024)

## Abstract (arXiv)

Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper, we provide a set of algorithms for materializing marginal statistics under the strong model of local differential privacy. We prove the first tight theoretical bounds on the accuracy of marginals compiled under each approach, perform empirical evaluation to confirm these bounds, and evaluate them for tasks such as modeling and correlation testing. Our results show that releasing information based on (local) Fourier transformations of the input is preferable to alternatives based directly on (local) marginals.