Authors: Xumeng Wang, Wei Chen, Huihua Guan, Wenlong Chen, Rusheng Pan, Jia-Kai Chou, Chris Bryan, Kwan-Liu Ma
Abstract: Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysiscan 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.
