Star-join query is the fundamental task in data warehouse and has wide applications in On-line Analytical Processing (olap) scenarios. Due to the large number of foreign key constraints and the asymmetric effect in the neighboring instance between the fact and dimension tables, even those latest dp efforts specifically designed for join, if directly applied to star-join query, will suffer from extremely large estimation errors and expensive computational cost. In this paper, we are thus motivated to propose DP-starJ, a novel Differentially Private framework for star-Join queries. DP-starJ consists of a series of strategies tailored to specific features of star-join, including 1) we unveil the different effects of fact and dimension tables on the neighboring database instances, and accordingly revisit the definitions tailored to different cases of star-join; 2) we propose Predicate Mechanism (PM), which utilizes predicate perturbation to inject noise into the join procedure instead of the results; 3) to further boost the robust performance, we propose a dp-compliant star-join algorithm for various types of star-join tasks based on PM. We provide both theoretical analysis and empirical study, which demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of accuracy, efficiency, and scalability.

In the hyperspectral image classification area, a few number of labeled samples is a bottleneck for the improvement of classification accuracy. In order to tackle this problem, multiple one-dimensional embedding interpolation (M1DEI) has been used for hyperspectral image classification and achieved promising results. Despite the success, the complexity of M1DEI prevents its practical application. On the other hand, the percentage of newly added samples is set by experience when enlarging the labeled set. In this paper we develop a method by extending the M1DEI method with local strategy, called multiple local one-dimensional embedding interpolation (ML1DEI). We only map the labeled samples and their local spatial neighbors into the one-dimensional (1D) space. The local strategy can reduce the complexity of M1DEI, since only labeled samples and their neighbors need to be mapped. In addition, the local strategy ensures all these newly labeled samples come from the spatial neighborhood of labeled samples. Then, during the merging stage, we can incorporate all of them with the labeled samples. Moreover, the proposed ML1DEI can incorporate the spatial information and make full use of the unlabeled samples. Compared with other spatial-spectral classification methods, the proposed ML1DEI method obtains promising results. Experimental results on the commonly used hyperspectral data set validate the effectiveness of the proposed method.