Privacy-Preserving Graph Analytics Engine

图挖掘可以带来巨大的社会和经济价值，但也会泄露关于个体的敏感信息。尽管差异隐私已成为私有数据分析的强有力的隐私保证，但现有的差异隐私系统和算法主要支持表格数据。由于图中信息的复杂性和多样性，将现有的隐私概念、算法和表格数据系统扩展到图数据面临许多挑战。到目前为止，还没有真正的图数据库系统可以支持私有图的分析。本研究的主要目标是构建一个隐私保护的图分析引擎，可以准确有效地回答私人图上的交互式查询，同时确保可证明的隐私保证。我们打算正式的图形数据的一种新颖的和可定制的隐私框架，支持交互式图形查询使用最先进的图形数据库系统，并使图形分析在分布式设置。

Graph mining can bring great social and economic values but leak sensitive information about individuals. Though differential privacy has appeared as a strong privacy guarantee for private data analysis, existing systems and algorithms for differential privacy mainly support tabular data. Due to the complex and diverse information within graphs, extending existing privacy notions, algorithms, and systems for tabular data to graph data face many challenges. So far, there are no real-world graph database systems that can support the analysis of the private graphs. The primary objective of this research is to build a privacy-preserving graph analytics engine that can answer interactive queries on private graphs accurately and efficiently while ensuring a provable privacy guarantee. We intend to formalize a novel and customizable privacy framework for graph data, support interactive graph queries using state-of-the-art graph database systems, and enable graph analysis in a distributed setting.