概化理论
对抗制
计算机科学
联动装置(软件)
个性化
身份(音乐)
构造(python库)
万维网
社会化媒体
互联网隐私
数据科学
人机交互
人工智能
心理学
基因
发展心理学
生物化学
化学
物理
声学
程序设计语言
作者
Peng Zhang,Qi Zhou,Tun Lu,Hansu Gu,Ning Gu
摘要
Cross-site user identity linkage (UIL) aims to link the identities of the same person across different social media platforms. Social media practitioners and service providers can construct composite user portraits based on cross-site UIL, which helps understand user behavior holistically and conduct accurate recommendations and personalization. However, many social media users expect each profile to stay within the platform where it was created and thus do not want the identities of different platforms to be linked. For this problem, we first investigate the approaches people would like to use to defend against cross-site UIL and the corresponding challenges. Based on the findings, we build an adversarial framework, DeLink, based on the thoughts of adversarial text generation to help people improve their social media screen names to defend against cross-site UIL. DeLink can support both Chinese and English languages and has good generalizability to the varying numbers of social media accounts and different cross-site user identity linkage models. Extensive evaluations validate DeLink’s better performance, including a higher success rate, higher efficiency, less impact on human perception, and capability to defend against different cross-site UIL models.
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