LF-GDPR: A Framework for Estimating Graph Metrics with Local Differential Privacy

Qingqing Ye, Haibo Hu, Man Ho Au, Xiaofeng Meng, Xiaokui Xiao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Local differential privacy (LDP) is an emerging technique for privacy-preserving data collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be easily applied to real-world graph analysis tasks such as community detection and centrality analysis due to its high implementation complexity and low data utility. In this paper, we address these two issues by presenting LF-GDPR, the first LDP-enabled graph metric estimation framework for graph analysis. It collects two atomic graph metrics --- the adjacency bit vector and node degree --- from each node locally. LF-GDPR simplifies the job of implementing LDP-related steps (e.g., local perturbation, aggregation and calibration) for a graph metric estimation task by providing either a complete or a parameterized algorithm for each step. To address low data utility of LDP, it optimally allocates privacy budget between the two atomic metrics during data collection. To demonstrate the usage of LF-GDPR, we show use cases on two common graph analysis tasks, namely, clustering coefficient estimation and community detection. The privacy and utility achieved by LF-GDPR are verified through theoretical analysis and extensive experimental results.

Original languageEnglish
JournalIEEE Transactions on Knowledge and Data Engineering
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Differential privacy
  • Estimation
  • Graph metric
  • Local differential privacy
  • Measurement
  • Perturbation methods
  • Privacy
  • Privacy-preserving graph analysis
  • Social networking (online)
  • Task analysis

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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