Contextualization is a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behaviour. Privacy and legal protection rights are a major challenge that needs to be tackled when capturing and using contextual data for recommendation. The privacy of learners can be protected through identity management. Participants can hold multiple identities or can adopt new pseudonymous personas. A pseudonymous actor needs a privacy-preserving mechanism for the transfer or merger of their reputation across their multiple pseudonyms. A reliable and trustworthy mechanism for reputation transfer (RT) from one persona to another is required. Such a reputation transfer model must preserve privacy and prevent link ability of learners' identities and personas. In this paper, we present an identity management-based solution to privacy and a privacy-preserving reputation management (RM) system which allows secure transfer of reputation. This paper includes the online rating calculation method for reputation management.

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Reputation-ranking Method Ranking Using Bayesian Approximation Trust And Reputation Relationship.