GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms

Xumeng Wang, Wei Chen, Jia Kai Chou, Chris Bryan, Huihua Guan, Wenlong Chen, Rusheng Pan, Kwan-Liu Ma

Research output: Contribution to journalArticle

5 Scopus citations


Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques—along with evaluating how applying the strategies will affect the utility of the anonymized results—remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.

Original languageEnglish (US)
JournalIEEE Transactions on Visualization and Computer Graphics
StateAccepted/In press - Aug 19 2018


  • Data privacy
  • Data visualization
  • Graph privacy
  • k-anonymity
  • Measurement
  • Pipelines
  • Privacy
  • privacy preservation
  • structural features
  • Task analysis
  • Visualization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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