SoK: Practical Aspects of Releasing Differentially Private Graphs

2026-03-19

AI Summary

This work addresses the longstanding tension between privacy and utility in graph data publishing, where existing differential privacy approaches often lack a unified evaluation framework, leading to potentially misleading privacy claims. To bridge this gap, the paper proposes the first systematic, goal-oriented framework for practical assessment. Through a comprehensive literature review, it identifies dominant methodologies and their critical vulnerabilities, then constructs a purpose-driven evaluation paradigm that enables informed method selection, clear interpretation, and fair comparison. The framework facilitates reproducible benchmarking in real-world scenarios such as social network analysis, thereby substantially enhancing the transparency, reliability, and practical utility of evaluating differentially private graph publishing techniques.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Evaluation and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Graph data is increasingly prevalent across domains, offering analytical value but raising significant privacy concerns. Edges may encode sensitive relationships, while node attributes may contain sensitive entity or personal data. Differential Privacy (DP) has gained traction for its strong guarantees, yet applying DP to graphs is challenging because of their complex relational structure, leading to trade-offs between privacy and utility. Existing methods vary in privacy definitions, utility goals, and contextual settings, complicating comparison. For practitioners, this is compounded by DP's interpretability issues, contributing to misleading protection claims. To address this, we propose a novel systemisation of existing methods tailored to practical considerations and adaptable to varying practitioner objectives. Our contributions include: (i) a comprehensive survey of differentially private graph release methods; (ii) identification of key vulnerabilities; and (iii) a practitioner-oriented, objective-based framework to guide the selection, interpretation, and sound evaluation of existing methods. We demonstrate the use of our systemisation through two exemplary scenarios in which we assume the role of a social network analyst, apply it, and conduct evaluations in accordance with our framework. Together, these two illustrative instantiations ultimately provide a unified benchmark for state-of-the-art methods in the social networks domain.

Problem

• Differential Privacy
• Graph Data
• Privacy-Utility Trade-off
• Practical Deployment
• Method Comparison

Innovation

• Differential Privacy
• Graph Release
• Systematisation of Knowledge
• Practitioner-oriented Framework
• Privacy-Utility Trade-off