Privacy-enhanced personalization for online learning

响应于人类用户的个人偏好或要求来个性化其动作的计算机系统通过构建用户的简档来实现这一点。这些配置文件基于从分析用户与系统的交互或从外部数据源获得的知识。在任何一种情况下，都存在对用户隐私的潜在威胁。我和其他人的研究一直致力于在保护用户隐私的同时提供个性化支持。一种关键的方法是将系统对用户个人身份的了解与被监控的行为分开。这可以通过为用户提供假名并将行为信息与该假名身份相关联来实现。但化名给个性化系统带来了社会和技术挑战。

Computer systems that personalize their actions in reaction to personal preferences or requirements of human users do so by building profiles of the users. These profiles are based on knowledge gained either from analysis of user interactions with the system or from outside data sources. In either circumstance, there is a potential threat to user privacy. Research by me and others has been devoted to providing personalization support while preserving user privacy. One key approach has been to separate the system's knowledge of the personal identity of the user from the behaviour that is being monitored. This can be achieved by providing a pseudonym for the user and associating behaviour information with the pseudonymous identity. But pseudonymity introduces both social and technical challenges for personalization systems.