Privacy-Preserving Graph Analytics Engine

隐私保护图分析引擎

• 批准号: 551061-2020
• 负责人: He, XiX
• 金额: $ 5.46万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750142
• 关键词: Privacy; Preserving; Graph; Analytics; Engine

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.

图挖掘可以带来巨大的社会和经济价值，但会泄露个人的敏感信息。尽管差分隐私已经成为隐私数据分析的强有力的隐私保障，但现有的差分隐私系统和算法主要支持表格数据。由于图表中的信息复杂多样，将表格数据的现有隐私概念、算法和系统扩展到图表数据面临着许多挑战。到目前为止，还没有现实世界的图数据库系统可以支持私有图的分析。这项研究的主要目标是构建一个保护隐私的图分析引擎，能够准确高效地回答私有图上的交互式查询，同时确保可证明的隐私保证。我们打算形式化一种新颖且可定制的图数据隐私框架，支持使用最先进的图数据库系统的交互式图查询，并在分布式设置中启用图分析。