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Abstract. Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data holder. However, no method has been proposed for the anonymization scenario of multiple independent data publishing. A data holder publishes a dataset, which contains overlapping population with other datasets published by other independent data holders. No existing methods are able to protect privacy in such multiple independent data publishing. In this paper we propose a new generalization principle (ρ, α)-anonymization that effectively overcomes the privacy concerns for multiple independent data publishing. We also develop an effective algorithm to achieve the (ρ, α)-anonymization. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves high quality data utility.

Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data owner. However, no method has been proposed for the anonymization of data of multiple independent data publications. A data owner publishes a dataset, which contains overlapping population with other datasets published by other independent data owners. In this paper we analyze the privacy risk in the such scenario and vulnerability of partitioned based anonymization methods. We show that no partitioned based anonymization methods can protect privacy in arbitrary data distributions, and identify a case that the privacy can be protected in the scenario. We propose a new generalization principle -cloning to protect privacy for multiple independent data publications. We also develop an effective algorithm to achieve the -cloning. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves good data utility.

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Personal Learning Environments (PLEs) help the learners to take control of their learning. PLEs enable the learners to set their own leaning targets and manage their learning by communicating with others in the process of learning. As latest technological advancements have brought revolution in every field of life, so as in the PLEs. Modern PLEs are the integration of a number of latest technologies i.e. blogs, Wikis, RSS feeds, where content is shaped as per the individual needs and interests of the students. Focusing on these latest aspects of the PLEs, University of South Australia initiated a three year new learning platform project in 2010, called LearnOnline, which will replace the University's current online teaching environment UniSAnet. LearnOnline was launched with a vision to foster richer learning through promoting students' active involvement in their courses and involving the students in a deeper learning experience. LearnOnline is built on modular approach and consists of different components i.e. ePortfolio, Course Outline, Lecture Recording, Copyright Monitoring, Student Email, Assessment and Feedback, Virtual Classroom, Course and Teacher Evaluation. Each component is developed separately and is fully independent. This methodology is helping the incremental implementation of the LearnOnline. As soon as a component is completed, after testing, it becomes the part of LearnOnline. In this paper, the author explains the features and workings of LearnOnline in detail and also evaluates its design methodologies.