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上的挑战。在基于计算机的学习环境中，如果个人信息的泄露可能导致不适当的刻板印象或歧视，潜在的其他学习者的骚扰，或者通过身份盗窃而导致的学术不诚实，保护学习者的隐私是重要的。在保护学习者隐私和获取学习支持系统有效工作所必需的和足够的知识之间存在权衡。在较大的学习社区中，个人信任关系可能不那么普遍(有时是不明智的)，需要一种保护用户隐私和衡量对他人信任的机制。在这里可以成功地应用伪恒等方法。对于许多学习者，特别是儿童，更好的自动化保护是合适的。识别和遏制学习者的危险行为的方法是必要的。在我的研究中，将研究保护隐私和模型信任的技术(适用于学习领域)。这包括通过身份管理为隐私增强型个性化开发安全可靠的模型，从一个假名身份到另一个身份的私人声誉转移模型，以及管理与单个学习者相关联的多个身份的模型。

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.  In computer-based learning environments, preserving learner privacy is important in situations where the release of personal information may result in inappropriate stereotypes or discrimination, potential harassment by other learners, or academic dishonesty through identity theft. There is a trade off between protecting learnerprivacy and gaining necessary and sufficient knowledge for a learning support system to work effectively. In larger learning communities where personal trust relationships may be less pervasive (andsometimes ill-advised), a mechanism for protecting user privacy and gauging trust in others is needed. Methods of pseudo-identity can be successfully applied here. For many learners, particularly children, greaterautomated protection is appropriate. Methods to identify and curb risky behaviour on the part of learners are needed.  Techniques for protecting privacy and modelling trust (appropriate for the learning domain) will be investigated in my research. This includes developing secure and reliable models for privacy-enhanced personalization through identity management, models for private reputation transfer from one pseudonymous identity to another, and models for managing multiple identities associated with a single learner.