Under the needs of processing huge amounts of data, providing high-quality service, and protecting user privacy in artificial intelligence of things (AIoT), federated learning (FL) has been treated as a promising technique to facilitate distributed learning with privacy protection. Although the importance of developing privacy-preserving FL has attracted a lot of attentions, the existing research only focuses on FL with independent identically distributed (i.i.d.) data and lacks study of non-i.i.d. scenario. What is worse, the assumption of i.i.d. data is impractical, reducing the performance of privacy protection in real applications. In this article, we carry out an innovative exploration of privacy protection in FL with non-i.i.d. data. First, a thorough analysis on privacy leakage in FL is conducted with proving the performance upper bound of privacy inference attack. Based on our analysis, a novel algorithm, 2DP-FL, is designed to achieve differential privacy by adding noise during training local models and when distributing global model. Especially, our 2DP-FL algorithm has a flexibility of noise addition to meet various needs and has a convergence upper bound. Finally, the real-data experiments can validate the results of our the oretical analysis and the advantages of 2DP-FL in privacy protection, learning convergence, and model accuracy.

Deep learning has an impressive performance of object detection and classification for autonomous vehicles. Nevertheless, the essential vulnerability of deep learning models to adversarial samples makes the autonomous vehicles suffer severe security and safety issues. Although a number of works have been proposed to study adversarial samples, only a few of them are designated for the scenario of autonomous vehicles. Moreover, the state-of-the-art attack models only focus on a single data source without considering the correlation among multiple data sources. To fill this blank, we propose two multi-source adversarial sample attack models, including the parallel attack model and the fusion attack model to simultaneously attack the image and LiDAR perception systems in the autonomous vehicles. In the parallel attack model, adversarial samples are generated from the original image and LiDAR data separately. In the fusion attack model, the adversarial samples of image and LiDAR can be generated from a low-dimension vector at the same time by fully exploring data correlation for data fusion and adversarial sample generation. Through comprehensive real-data experiments, we validate that our proposed models are more powerful and efficient to break down the perception systems of autonomous vehicles compared with the state-of-the-art. Furthermore, we simulate possible attack scenarios in Vehicular Ad hoc Networks (VANETs) to evaluate the attack performance of our proposed methods.

Computer vision and deep neural networks have been significantly promoting the development of visual perception in these years. Particularly, for autonomous vehicles, real-time image/video data is captured by onboard cameras and analyzed by computer vision techniques in many real applications. In the captured camera data, some contents can be used as auxiliary information to infer individuals' locations and trajectories, which leads to severe privacy leakage but has been rarely studied. Thus, the goal of this article is to protect individuals' location privacy by hiding side-channel information in the captured data while preserving the data utility for downstream applications. To this end, the technology of generative adversarial networks (GAN) is utilized to design two novel models, named ADGAN-I and ADGAN-II, both of which can take the original camera data as inputs and generate privacy-preserving outputs according to predefined sensitive object class. Thus, the processed camera data can defend location inference attack from adversaries in offline applications. Moreover, in ADGAN-I and ADGAN-II, the tradeoff between location privacy and data utility can be effectively balanced. Finally, the results of extensive real-data experiments validate the superiority of our proposed models over the state of the arts in utility preservation and privacy protection for autonomous vehicles' images and videos.

The study of generative models is a promising branch of deep learning techniques, which has been successfully applied to different scenarios, such as Artificial Intelligence and the Internet of Things. While in most of the existing works, the generative models are realized as a centralized structure, raising the threats of security and privacy and the overburden of communication costs. Rare efforts have been committed to investigating distributed generative models, especially when the training data comes from multiple heterogeneous sources under realistic IoT settings. In this paper, to handle this challenging problem, we design a federated generative model framework that can learn a powerful generator for the hierarchical IoT systems. Particularly, our generative model framework can solve the problem of distributed data generation on multi-source heterogeneous data in two scenarios, i.e., feature related scenario and label related scenario. In addition, in our federated generative models, we develop a synchronous and an asynchronous updating methods to satisfy different application requirements. Extensive experiments on a simulated dataset and multiple real datasets are conducted to evaluate the data generation performance of our proposed generative models through comparison with the state-of-the-arts.

Recent progress has witnessed the excellent success of neural networks in many emerging applications, such as image recognition, text classification, and speech analysis. In order to achieve secure communication, the utilization of neural networks has been realized yet has not raised sufficient research attention. Additionally, the existing neural network-based communication system falls short due to its critical security flaws. In this paper, we investigate the security vulnerabilities of the existing neural communication system. Based on our analysis, we design two kinds of attack models, including target man-in-the-middle attack and target fraud attack . After that, to improve the security performance of neural communication systems, we develop a new defense mechanism to facilitate two-way secure communication by separating secret key from plaintext and incorporating defensive loss into the training process. Moreover, we show the effectiveness of our proposed neural communication system via theoretical proof. Finally, we implement comprehensive real data experiments to evaluate the performance of our attack and defense methods from the aspects of classification accuracy, communication efficiency and communication qualify, which confirms the advantages of our proposed neural communication system compared with the state-of-the-art.