Privacy-enhanced personalization for online learning

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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.