Private Graph Data Release: A Survey

TL;DR

This survey reviews algorithms for private graph data release, focusing on differential privacy and related frameworks, highlighting their applications across various domains to balance privacy and utility.

Contribution

It provides a comprehensive taxonomy of private graph data release mechanisms, including extensions of differential privacy and applications in multiple fields.

Findings

Extensive survey of privacy-preserving graph data release algorithms

Analysis of differential privacy and Pufferfish Privacy frameworks

Application insights across social, financial, and healthcare domains

Abstract

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.