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A variety of services utilizing users' positions have become available because of rapid advances in Global Positioning System (GPS) technologies. Since location information may reveal private information, preserving location privacy has become a significant issue. We proposed a dummy-based method of anonymizing location to protect this privacy in our previous work that generated dummies based on various restrictions in a real environment. However, the previous work assumed a simplified mobility model in which users kept moving and did not stop. If we assume a more realistic mobility model in which users often pause to visit various attractions, it becomes increasingly more difficult to generate dummies that will move naturally. In this paper, we assumed that the users' movements are known in advance and propose a dummy-based anonymization method based on user movements, where dummies move naturally while stopping at several locations. We simulated user movements on real map information and verified the method we propose was more effective than the previous one.

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The NTCIR INTENT task comprises two subtasks: Subtopic Mining, where systems are required to return a ranked list of subtopic strings for each given query; and Document Ranking, where systems are required to return a diversified web search result for each given query. This paper summarises the novel features of the Second INTENT task at NTCIR-10 and its main findings, and poses some questions for future diversified search evaluation.