Tao Zhang

The collaborative construction of large-scale, diverse datasets is crucial for developing high-performance machine learning models. However, this collaboration faces significant challenges, including ensuring data security, protecting participant privacy, maintaining high dataset quality, and aligning economic incentives among multiple stakeholders. Effective risk management strategies are essential to systematically identify, assess, and mitigate potential risks associated with data collaboration. This study proposes a federated blockchain-based framework designed to manage multiparty dataset collaborations securely and transparently, explicitly incorporating comprehensive risk management practices. The proposed framework involves six core entities—key distribution center (KDC), researcher (RA), data owner (DO), consortium blockchain, dataset evaluation platform, and the orchestrating model itself—to ensure secure, privacy-preserving and high-quality dataset collaboration. In addition, the framework uses blockchain technology to guarantee the traceability and immutability of data transactions, integrating token-based incentives to encourage data contributors to provide high-quality datasets. To systematically mitigate dataset quality risks, we introduced an innovative categorical dataset quality assessment method leveraging label reordering to robustly evaluate datasets. We validated this quality assessment approach using both publicly available (UCI) and privately constructed datasets. Furthermore, our research implemented the proposed blockchain-based management system within a consortium blockchain infrastructure, benchmarking its performance against existing methods to demonstrate enhanced security, reliability, risk mitigation effectiveness, and incentive alignment in dataset collaboration.

With the growth of IoT technology, connected devices have surged, increasing security risks, especially for devices lacking authentication. Anonymization protection prevents data leaks and control theft but traditional methods lack dynamism, struggle to balance privacy and availability, and remain vulnerable to targeted attacks. Anonymization protection techniques can prevent the leakage of sensitive information and the theft of control privileges, significantly improving the security of devices. However, traditional anonymity protection methods lack dynamism, making it difficult to trade-off between data availability and privacy protection, and attackers can discover system vulnerabilities through reconnaissance and analysis, leaving devices still vulnerable to targeted attacks. In this paper, we propose a device identity anonymization protection method based on address hopping, using the address hopping policy in the Mobile Target Defense (MTD) technique. It collects network topology and node state information, constructs a virtual network topology by backtracking method, and periodically replaces the paths and addresses under the satisfaction of specific constraints, so as to realize the anonymity of network devices. It effectively reduces the risk of device attacks, optimizes network performance, and maintains data availability by dynamically adjusting device addresses in the network. Experiments using Mininet and Ryu controllers show the approach significantly reduces host scans and data exposure compared to unprotected policies.

Risk assessment techniques have been widely used in edge intelligent control platforms from security protection to decision optimization. In recent years, many researchers have applied machine learning or deep learning to risk assessment in edge intelligent control platforms. However, these approaches face significant challenges, including high computational resource requirements, high data dependency, and poor interpretability. In this paper, we propose a risk assessment method for edge intelligent control platforms based on hybrid game theory, which constructs a two-dimensional security risk assessment framework across cyber and physical domains. The attack tree model is first utilized to meticulously outline the potential attack paths and integrate the mixed-strategy game between attackers and defenders at the leaf nodes. Then, through game-theoretic analysis, the payoff functions of both parties are established, Nash equilibrium is determined to predict strategic choices, and the fuzzy analytic hierarchy process (FAHP) is combined with the CRITIC weighting method to quantify the risk associated with each node in the attack tree model. Finally, the risk values of the root nodes are aggregated to assess the overall security level of the platform. This approach effectively simulates real-world adversarial interactions and improves the accuracy and practicality of risk assessment.

Anonymous access control is crucial in network security to protect user privacy and support anonymous communication while balancing the need for confidentiality and traceability in specific scenarios. This control mechanism is important for maintaining data security, identity protection and security auditing. Traditional anonymous access control methods currently have two challenges: High performance overhead and Vulnerability to cyber-attacks. To cope with the above challenges, this paper proposes a dynamic anonymous access control method based on dual-mode single-packet authentication. The method utilizes dual-mode single-packet authentication to complete user authentication in the anonymous access phase, dynamically evaluates the user’s trust level through the trust evaluation module, and finally decides whether to allow the user to access the network resources. Experimentally verified, the dual-mode single-packet authentication scheme is more advantageous than other schemes regarding communication overhead and computation overhead, and the access efficiency is further improved.

Industrial control systems may be affected by vulnerabilities, leading to a series of security issues. However, existing vulnerability remediation methods mainly include static patching and dynamic patching, but due to the hardware resource constraints and complex application environments faced by embedded devices, these traditional methods all suffer from the limitations of high risk of service interruption and high resource overhead, which make it difficult to satisfy the special needs of embedded devices in terms of security and continuity assurance. For this reason, this paper proposes a hot-patch-based vulnerability repair method for embedded devices. The method utilizes hardware built-in functions to perform vulnerability repair during system runtime, effectively reducing system downtime and optimizing resource consumption during the repair process. Specifically, the repair method proposed in this paper contains four steps: vulnerability identification, vulnerability localization, patch preparation and patch switching. By using the onboard debugging unit to replace instructions at runtime, thus avoiding device downtime and system reboot, the system continuity of the embedded device is guaranteed.The experimental results show that this method achieves efficient and secure vulnerability repair while meeting the real-time and resource constraints of embedded devices, providing an innovative and effective way to improve the security and reliability of embedded devices.