Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 25, Issue 1, Pages 193-203Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2018.2865021
Keywords
Graph privacy; k-anonymity; structural features; privacy preservation
Categories
Funding
- National 973 Program of China [2015CB352503]
- National Natural Science Foundation of China [61772456, 61761136020]
- Alibaba-Zhejiang University Joint Institute of Frontier Technologies
- U.S. National Science Foundation [IIS-1320229, IIS-1741536]
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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.
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