期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 25, 期 1, 页码 193-203出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2018.2865021
关键词
Graph privacy; k-anonymity; structural features; privacy preservation
资金
- 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]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据