YOLO has a fast detection speed and is suitable for object detection in real-time environment. This paper is based on YOLO v3 network and applied to parking spaces and vehicle detection in parking lots. Based on YOLO v3, this paper adds a residual structure to extract deep vehicle parking space features, and uses four different scale feature maps for object detection, so that deep networks can extract more fine-grained features. Experiment results show that this method can improve the detection accuracy of vehicle and parking space, while reducing the missed detection rate.

k Nearest neighbor (kNN) classification algorithm is a prediction model which is widely used for real‐life applications, such as healthcare, finance, computer vision, personalization recommendation and precision marketing. The arrival of data explosion era results in the significant increase of feature dimension, which also makes for the increase of privacy concern over the available samples and unlabeled data in the applications of machine learning. In this paper, we present a secure low communication overhead kNN classification protocol that is able to deal with high‐dimensional features given in real numbers. First, to deal with feature values given in real numbers, we develop a specific data conversion algorithm, which is used in the chosen fully homomorphic scheme. This conversion algorithm is generic and applicable to other algorithms that need to handle real numbers using the fully homomorphic scheme. Second, we present a privacy‐preserving euclidean distance protocol (PPEDP), which works with the Euclidean distance computation between two points given in real numbers in a high‐dimensional space. Then, based on the novelty PPEDP and oblivious transfer, we propose a new classification approach, efficient secure kNN classification protocol, (ESkNN) with low communication overhead, which is appropriate for a sample set with high‐dimensional features and real number feature values. Moreover, we implement ESkNN in C++. Experimental results show that ESkNN is several orders of magnitude faster in performance than existing works, and scales up to 18 000 feature dimension in a memory limited environment.

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In recent years, blockchain and machine-learning techniques have received increasing attention both in theoretical and practical aspects. However, the applications of these techniques have many challenges, one of which is the privacy-preserving issue. In this paper, we focus on, specifically, the privacy-preserving issue of imbalanced datasets, a commonly found problem in real-world applications. Built based on the fully homomorphic encryption technique, this paper presents two new secure protocols, Privacy-Preserving Synthetic Minority Oversampling Protocol (PPSMOS) and Borderline Privacy-Preserving Synthetic Minority Oversampling Protocol (Borderline-PPSMOS). Our analysis reveals that PPSMOS is generally more efficient in performance than Borderline-PPSMOS. However, Borderline-PPSMOS achieves a better TP rate and F-Value than PPSMOS.