Privacy-enhanced personalization for online learning

在线学习的隐私增强个性化

• 批准号: 41799-2007
• 负责人: Greer, Jim
• 金额: $ 2.08万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=361742
• 关键词: Privacy; enhanced; personalization; online; learning

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.

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这样做。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。无论哪种情况，都可能对用户隐私造成威胁。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。一个关键的方法是将系统对用户个人身份的了解与被监视的行为分开。这可以通过为用户提供假名并将行为信息与假名身份相关联来实现。但是，匿名性为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，保护学习者隐私是很重要的，因为个人信息的泄露可能会导致不适当的刻板印象或歧视，其他学习者的潜在骚扰，或通过身份盗窃造成学术上的不诚实。在保护学习者隐私和获得必要和足够的知识以使学习支持系统有效工作之间存在权衡。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他人信任的机制。伪恒等式的方法可以成功地应用于这里。对于许多学习者，特别是儿童，最好的自动保护是适当的。需要识别和抑制学习者的危险行为的方法。在我的研究中，将研究保护隐私和建模信任（适用于学习领域）的技术。这包括通过身份管理开发安全可靠的隐私增强个性化模型，从一个匿名身份到另一个匿名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。