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

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...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2019-01, Vol.25 (1), p.193-203
Main Authors: Xumeng Wang, Wei Chen, Jia-Kai Chou, Bryan, Chris, Huihua Guan, Wenlong Chen, Rusheng Pan, Kwan-Liu Ma
Format: Article
Language:English
Subjects:
Data privacy
Data visualization
Graph privacy
k-anonymity
Measurement
Pipelines
Privacy
privacy preservation
structural features
Tactics
Task analysis
Visualization
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Summary: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 GraphPro tector, we report several case studies and feedback collected from interviews with expert users in various scenarios.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2018.2865021