Machine learning classification is an useful tool for trend prediction by analyzing big data. As supporting homomorphic operations over encrypted data without decryption, fully homomorphic encryption (FHE) contributes to machine learning classification without leaking user privacy, especially in the outsouring scenario. In this paper, we propose an improved FHE scheme based on HElib, which is a FHE library implemented based on Brakerski's FHE scheme. Our improvement focuses on two aspects. On the one hand, we first use the relinearization technique to reduce the ciphertext size, and then the modulus switching technique is used to reduce the modulus and decryption noise. On the other hand, we need no relinearization and modulus switching if there is additive homomorphic or no homomorphic operation in the multiplicative ciphertext's next homomorphic operation. Homomorphic comparison protocol, private hyperplane decision-based classification and private Naïve Bayes classification are implemented by additive homomorphic and multiplicative homomorphic firstly. In our homomorphic comparison protocol, the number of interactions is reduced from 3 to 1. We choose the proposed FHE scheme to implement private decision tree classification. Simulation results show that the efficiency of our FHE scheme and implementation of private decision tree classification are more efficient than other two schemes.

In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of data dissemination and computation. However, encrypted vehicle data cannot be analyzed easily and flexibly. Because homomorphic encryption supports computations of the ciphertext, it can completely solve this problem. In this paper, we provide a comprehensive survey of secure computation based on homomorphic encryption in VANETs. We first describe the related definitions and the current state of homomorphic encryption. Next, we present the framework, communication domains, wireless access technologies and cyber-security issues of VANETs. Then, we describe the state of the art of secure basic operations, data aggregation, data query and other data computation in VANETs. Finally, several challenges and open issues are discussed for future research.

Existing high-precision object detection algorithms for UAV (unmanned aerial vehicle) aerial images often have a large number of parameters and heavy weight, which makes it difficult to be applied to mobile devices. We propose three YOLO-based lightweight object detection networks for UAVs, named YOLO-L, YOLO-S, and YOLO-M, respectively. In YOLO-L, we adopt a deconvolution approach to explore suitable upsampling rules during training to improve the detection accuracy. The convolution-batch normalization-SiLU activation function (CBS) structure is replaced with Ghost CBS to reduce the number of parameters and weight, meanwhile Maxpool maximum pooling operation is proposed to replace the CBS structure to avoid generating parameters and weight. YOLO-S greatly reduces the weight of the network by directly introducing CSPGhostNeck residual structures, so that the parameters and weight are respectively decreased by about 15% at the expense of 2.4% mAP. And YOLO-M adopts the CSPGhostNeck residual structure and deconvolution to reduce parameters by 5.6% and weight by 5.7%, while mAP only by 1.8%. The results show that the three lightweight detection networks proposed in this paper have good performance in UAV aerial image object detection task.

Hyperspectral image (HSI) classification has attracted widespread concern in recent years. However, due to the complexity of the HSI gathering environment, it is difficult to obtain a great number of HSI labeled samples. Therefore, how to effectively extract the spatial–spectral feature with small-scale training samples is the crucial point of HSI classification. In this paper, a novel fusion framework for small-sample HSI classification is proposed to fully combine the advantages of multidimensional CNN and handcrafted features. Firstly, a 3D fuzzy histogram of oriented gradients (3D-FHOG) descriptor is proposed to fully extract the handcrafted spatial–spectral feature of HSI pixels, which is suggested to be more robust by overcoming the local spatial–spectral feature uncertainty. Secondly, a multidimensional Siamese network (MDSN), which is updated by minimizing both contrastive loss and classification loss, is designed to effectively exploit the CNN-based spatial–spectral features from multiple dimensions. Finally, the proposed MDSN combined with 3D-FHOG is utilized for small-sample HSI classification to verify the effectiveness of our proposed fusion framework. The experimental results on three public data sets indicate that the proposed MDSN combined with 3D-FHOG is significantly better than the representative handcrafted feature-based and CNN-based methods, which in turn demonstrates the superiority of the proposed fusion framework.

This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is intro