# Private Graph Data Release: A Survey

arXiv: https://arxiv.org/abs/2107.04245 · HTML: https://mlsanity.com/paper/2107.04245/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2021.

## Built on by (most cited first, 15 of 15)

- [Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities](https://mlsanity.com/paper/2506.18019/) (2025)
- [Differentially private exact recovery for stochastic block models](https://mlsanity.com/paper/2406.02644/) (2024)
- [Differentially Private Graph Diffusion with Applications in Personalized PageRanks](https://mlsanity.com/paper/2407.00077/) (2024)
- [On the Inherent Privacy Properties of Discrete Denoising Diffusion Models](https://mlsanity.com/paper/2310.15524/) (2023)
- [MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage](https://mlsanity.com/paper/2401.12824/) (2024)
- [On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models](https://mlsanity.com/paper/2505.05816/) (2025)
- [Privacy Preserving Reinforcement Learning for Population Processes](https://mlsanity.com/paper/2406.17649/) (2024)
- [Privately Learning from Graphs with Applications in Fine-tuning Large Language Models](https://mlsanity.com/paper/2410.08299/) (2024)
- [Chase Anonymisation: Privacy-Preserving Knowledge Graphs with Logical Reasoning](https://mlsanity.com/paper/2410.12418/) (2024)
- [Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts](https://mlsanity.com/paper/2507.14835/) (2025)
- [Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation](https://mlsanity.com/paper/2412.10130/) (2024)
- [Signal Recovery from Random Dot-Product Graphs Under Local Differential Privacy](https://mlsanity.com/paper/2504.17274/) (2025)
- [Differentially Private and Scalable Estimation of the Network Principal Component](https://mlsanity.com/paper/2505.03858/) (2025)
- [Differentially Private Relational Learning with Entity-level Privacy Guarantees](https://mlsanity.com/paper/2506.08347/) (2025)
- [Empirical Auditing of Edge-Private Graph Generators](https://mlsanity.com/paper/2609.25155/) (2026)

## Abstract (arXiv)

The application of graph analytics to various domains has yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect private information in graph data, especially in light of the many privacy breaches in real-world graph data that was supposed to preserve sensitive information. This paper provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific focus on provably private mechanisms. Many of these mechanisms are natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that address some of the limitations of Differential Privacy. We also provide a wide-ranging survey of the applications of private graph data release mechanisms to social networks, finance, supply chain, and health care. This survey paper and the taxonomy it provides should benefit practitioners and researchers alike in the increasingly important area of private analytics and data release.