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In recent years, the problem of privacy preservation on published social networks has become more and more important. The social media data are available on the popular on-line social network websites with rich personal information, which can be analyzed for connectivity and user behaviors. In this work, we study the problem of preserving sensitive paths in social networks. We examine the new concept...
In this paper, we propose an efficient projection-based algorithm to discover high sequential utility patterns from quantitative sequence databases. An effective pruning strategy in the proposed algorithm is designed to tighten upper-bounds for subsequences in mining. By using the strategy, a large number of unpromising subsequences could be pruned to improve execution efficiency. Finally, the experimental...
In the past, the multiple fuzzy frequent pattern tree (MFFP tree) was proposed for extracting multiple fuzzy frequent itemsets from quantitative transactions. It kept the multiple transformed fuzzy regions of an item to form the multiple fuzzy frequent itemsets. In this paper, an incremental algorithm is proposed for efficiently mining multiple fuzzy frequent itemsets based on the FUP concepts and...
To preserve privacy, k-anonymity on relational, set-valued, and graph data have been studied extensively in recent years. Information on social networks can be modeled as un-weighted or weighted graph data for sharing and publishing. We have previously proposed k-anonymous path privacy concept on weighted social graphs to preserve privacy of the shortest path [9]. A published social network graph...
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