This dissertation aims to systematically research the "trustworthy" Multiagent and Machine Learning systems in the context of the Internet of... Show moreThis dissertation aims to systematically research the "trustworthy" Multiagent and Machine Learning systems in the context of the Internet of Things (IoT) system, which mainly consists of two aspects: data privacy and robustness. Specifically, data privacy concerns about the protection of the data in one given system, i.e., the data identified to be sensitive or private cannot be disclosed directly to others; robustness refers to the ability of the system to defend/mitigate the potential attacks/threats, i.e., maintaining the stable and normal operation of one system.Starting from the smart grid, a representative multiagent system in the IoT, I demonstrate two works on improving data privacy and robustness in aspects of different applications, load balancing and energy trading, which integrates secure multiparty computation (SMC) protocols for normal computation to ensure data privacy. More significantly, the schemes can be readily extended to other applications in IoT, e.g., connected vehicles, mobile sensing systems.For the machine learning, I have studied two main areas, i.e., computer vision and natural language processing with the privacy and robustness correspondingly. I first present the comprehensive robustness evaluation study of the DNN-based video recognition systems with two novel proposed attacks in both test and training phase, i.e., adversarial and poisoning attacks. Besides, I also propose the adaptive defenses to fully evaluate such two attacks, which can thus further advance the robustness of system. I also propose the privacy evaluation for the language systems and show the practice to reveal and address the privacy risks in the language models. Finally, I demonstrate a private and efficient data computation framework with the cloud computing technology to provide more robust and private IoT systems. Show less
