Data mining has heralded the major breakthrough in data analysis, serving as a “super cruncher” to discover hidden information and valuable knowledge in big data systems. For many applications, the collection of big data usually involves various parties who are interested in pooling their private data sets together to jointly train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to disclose their own data due to privacy concerns, making it imperative to provide privacy guarantee in collaborative data mining over distributed data sets. In this paper, we focus on tree-based data mining. To begin with, we design novel privacy-preserving schemes for two most common tasks: regression and binary classification, where individual data owners can perform training locally in a differentially private manner. Then, for the first time, we design and implement a privacy-preserving system for gradient boosting decision tree (GBDT), where different regression trees trained by multiple data owners can be securely aggregated into an ensemble. We conduct extensive experiments to evaluate the performance of our system on multiple real-world data sets. The results demonstrate that our system can provide a strong privacy protection for individual data owners while maintaining the prediction accuracy of the original trained model.

Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such as a CCD camera, depth sensors, such as a Microsoft Kinect, or inertial sensors, such as an accelerometer. However, a single type of sensors may only capture part of the dynamic gait features and make the gait recognition sensitive to complex covariate conditions, leading to fragile gait-based person identification systems. In this paper, we propose to combine all three types of sensors for gait data collection and gait recognition, which can be used for important identification applications, such as identity recognition to access a restricted building or area. We propose two new algorithms, namely EigenGait and TrajGait, to extract gait features from the inertial data and the RGBD (color and depth) data, respectively. Specifically, EigenGait extracts general gait dynamics from the accelerometer readings in the eigenspace and TrajGait extracts more detailed subdynamics by analyzing 3-D dense trajectories. Finally, both extracted features are fed into a supervised classifier for gait recognition and person identification. Experiments on 50 subjects, with comparisons to several other state-of-the-art gait-recognition approaches, show that the proposed approach can achieve higher recognition accuracy and robustness.

Human vision benefits a lot from pattern collocations in visual activities such as object detection and recognition. Usually, pattern collocations display as the co-occurrences of visual primitives, e.g., colors, gradients, or textons, in neighboring regions. In the past two decades, many sophisticated local feature descriptors have been developed to describe visual primitives, and some of them even take into account the co-occurrence information for improving their discriminative power. However, most of these descriptors only consider feature co-occurrence within a very small neighborhood, e.g., 8-connected or 16-connected area, which would fall short in describing pattern collocations built up by feature co-occurrences in a wider neighborhood. In this paper, we propose to describe local pattern collocations by using a new and general regional co-occurrence approach. In this approach, an input image is first partitioned into a set of homogeneous superpixels. Then, features in each superpixel are extracted by a variety of local feature descriptors, based on which a number of patterns are computed. Finally, pattern co-occurrences within the superpixel and between the neighboring superpixels are calculated and factorized into a final descriptor for local pattern collocation. The proposed regional co-occurrence framework is extensively tested on a wide range of popular shape, color, and texture descriptors in terms of image and object categorizations. The experimental results have shown significant performance improvements by using the proposed framework over the existing popular descriptors.

With the popular use of high-resolution satellite images, more and more research efforts have been placed on remote sensing scene classification/recognition. In scene classification, effective feature selection can significantly boost the final performance. In this letter, a novel deep-learning-based feature-selection method is proposed, which formulates the feature-selection problem as a feature reconstruction problem. Note that the popular deep-learning technique, i.e., the deep belief network (DBN), achieves feature abstraction by minimizing the reconstruction error over the whole feature set, and features with smaller reconstruction errors would hold more feature intrinsics for image representation. Therefore, the proposed method selects features that are more reconstructible as the discriminative features. Specifically, an iterative algorithm is developed to adapt the DBN to produce the inquired reconstruction weights. In the experiments, 2800 remote sensing scene images of seven categories are collected for performance evaluation. Experimental results demonstrate the effectiveness of the proposed method.