Private Graph Data Release: A Survey

The application of graph analytics to various domains have 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 databases, 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 fall under natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that can deal with the limitations of Differential Privacy. A wide-ranging survey of the applications of private graph data release mechanisms to social networks, finance, supply chain, health and energy is also provided. This survey paper and the taxonomy it provides should benefit practitioners and researchers alike in the increasingly important area of private graph data release and analysis

AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy

For protecting users' private data, local differential privacy (LDP) has been leveraged to provide the privacy-preserving range query, thus supporting further statistical analysis. However, existing LDP-based range query approaches are limited by their properties, i.e., collecting user data according to a pre-defined structure. These static frameworks would incur excessive noise added to the aggregated data especially in the low privacy budget setting. In this work, we propose an Adaptive Hierarchical Decomposition (AHEAD) protocol, which adaptively and dynamically controls the built tree structure, so that the injected noise is well controlled for maintaining high utility. Furthermore, we derive a guideline for properly choosing parameters for AHEAD so that the overall utility can be consistently competitive while rigorously satisfying LDP. Leveraging multiple real and synthetic datasets, we extensively show the effectiveness of AHEAD in both low and high dimensional range query scenarios, as well as its advantages over the state-of-the-art methods. In addition, we provide a series of useful observations for deploying AHEAD in practice