Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI), where agents interact with environments to learn policies for solving complex tasks. In recent years, DRL has achieved remarkable breakthroughs in various tasks, including video games, robotic control, quantitative trading, and autonomous driving. Despite its accomplishments, security and privacy-related issues still prevent us from deploying trustworthy DRL applications. For example, by manipulating the environment, an attacker can influence an agent’s actions, misleading it to behave abnormally. Additionally, an attacker can infer private training data and environmental information by maliciously interacting with DRL models, causing a privacy breach. In this survey, we systematically investigate the recent progress of security and privacy issues in the context of DRL. First, we present a holistic review of security-related attacks within DRL systems from the perspectives of single-agent and multi-agent systems and review privacy-related attacks. Second, we review and classify defense methods used to address security-related challenges, including robust learning, anomaly detection, and game theory approaches. Third, we review and classify privacy-preserving technologies, including encryption, differential privacy, and policy confusion. We conclude the survey by discussing open issues and possible directions for future research in this field.

In modern information systems, real-time streaming data are generated in various vertical application scenarios, such as industrial security cameras, household intelligent devices, mobile robots, and among others. However, these low-end devices can hardly provide real-time and accurate data analysis functionalities due to their limited onboard performances. Traditional centralized server computing also suffers from its prolonged transmission latency, resulting in huge response time. To deal with this problem, this article presents a novel distributed computation intelligent system with nearby edge devices, abbreviated as EdgeStreaming, to facilitate rapid and accurate analysis of streaming data. First, we thoroughly explore the available edge devices surrounding the terminal to generate an internally interconnected edge network. This edge network real-time perceives and updates the internal resource status of each edge device, such as computational and storage resources. Dynamic allocation of external computational or storage demands can be made based on the current load of individual edge devices. Consequently, the streaming data perceived by external terminal devices can be transmitted in real-time to any edge gateway. The edge network employs a well-designed task scheduling strategy to partition and allocate streaming data processing demands to one or multiple edge devices. Additionally, it customizes computational requirements judiciously, for instance, by utilizing model compression to expedite computation speed. We deployed an edge network comprising multiple Raspberry Pis, NVIDIA Jetson Nano, and Jetson NVIDIA TX2 devices, successfully achieving real-time analysis and detection of video streaming data. We believe our work provides new technological support for the real-time processing of streaming data.

State-of-the-art watermarking and fingerprinting techniques for Large Language Models (LLMs) are explored, with our analysis spanning a wide array of methodologies designed to protect the intellectual property of LLMs. The review of watermarking techniques is based on embedding watermarks during the training, logits generation, and token sampling phases. Meanwhile, we investigate the application of watermarking technology in multimodal LLMs and potential attacks on watermarks. Moreover, our examination of fingerprinting techniques revealed the ingenuity behind methods used to identify LLMs. We discussed the development of fingerprints based on model behavior and using deep learning models to learn thresholds for fingerprint comparison. Our survey has underscored the importance of advancing security measures for LLMs, especially in light of the increasing sophistication of adversarial attacks. As LLMs continue to play a pivotal role in advancing AI technologies, developing and refining security measures that safeguard their intellectual property and ensure their ethical deployment is imperative.