GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms
| Autor: | Jia-Kai Chou, Kwan-Liu Ma, Wei Chen, Rusheng Pan, Huihua Guan, Chris Bryan, Wenlong Chen, Xumeng Wang |
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| Rok vydání: | 2019 |
| Předmět: |
Information privacy
Information retrieval Social network business.industry Computer science k-anonymity Adversary Computer Graphics and Computer-Aided Design Visualization Data visualization Signal Processing Task analysis Graph (abstract data type) Computer Vision and Pattern Recognition business Software |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 25:193-203 |
| ISSN: | 2160-9306 1077-2626 |
| DOI: | 10.1109/tvcg.2018.2865021 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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