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This paper studies the problem of top-k distance-based outlier detection on uncertain data. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. We start with the Naive approach. We then introduce a populated-cell list (PC-list), a sorted list of non-empty cells of a grid (grid is used to index our data). Using PC-list, our top-k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top-k outliers. An approximate top-k outlier detection algorithm is also presented to further increase the efficiency of our outlier detection algorithm. An extensive empirical study on synthetic and real datasets shows that our proposed approaches are efficient and scalable.