# Differential Privacy of Hierarchical Census Data: An Optimization Approach

arXiv: https://arxiv.org/abs/2006.15673 · HTML: https://mlsanity.com/paper/2006.15673/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

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

- [Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey](https://mlsanity.com/paper/2202.08187/) (2022)
- [DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?](https://mlsanity.com/paper/2106.12576/) (2021)
- [Decision Making with Differential Privacy under a Fairness Lens](https://mlsanity.com/paper/2105.07513/) (2021)
- [Differential Privacy for Credit Risk Model](https://mlsanity.com/paper/2106.15343/) (2021)
- [Human-imperceptible, Machine-recognizable Images](https://mlsanity.com/paper/2306.03679/) (2023)
- [Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory](https://mlsanity.com/paper/2309.03487/) (2023)
- [Differentially Private Range Queries with Correlated Input Perturbation](https://mlsanity.com/paper/2402.07066/) (2024)
- [DM4Steal: Diffusion Model For Link Stealing Attack On Graph Neural Networks](https://mlsanity.com/paper/2411.03364/) (2024)
- [Optimal Allocation of Privacy Budget on Hierarchical Data Release](https://mlsanity.com/paper/2505.10871/) (2025)

## Abstract (arXiv)

This paper is motivated by applications of a Census Bureau interested in releasing aggregate socio-economic data about a large population without revealing sensitive information about any individual. The released information can be the number of individuals living alone, the number of cars they own, or their salary brackets. Recent events have identified some of the privacy challenges faced by these organizations. To address them, this paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals. The counts are reported at multiple granularities (e.g., the national, state, and county levels) and must be consistent across all levels. The core of the mechanism is an optimization model that redistributes the noise introduced to achieve differential privacy in order to meet the consistency constraints between the hierarchical levels. The key technical contribution of the paper shows that this optimization problem can be solved in polynomial time by exploiting the structure of its cost functions. Experimental results on very large, real datasets show that the proposed mechanism provides improvements of up to two orders of magnitude in terms of computational efficiency and accuracy with respect to other state-of-the-art techniques.