Privacy-Preserving Graph Analytics Engine

隐私保护图分析引擎

• 批准号: 551061-2020
• 负责人: He, Xi
• 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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720804
• 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，但也会泄露个人的敏感信息。虽然差分隐私已经出现作为一个强大的隐私保护的私人数据分析，现有的系统和算法的差分隐私主要支持表格数据。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。