Abstract Private Information Retrieval (PIR) is a cryptographic technique that allows Data User (DU) to retrieve data from a SERVER without revealing which specific data item is being accessed. Traditional PIR protocols typically assume that the data is locally stored and directly controlled by Data Owner (DO), but in real-world scenarios, data is often hosted on untrusted third-party SERVERs, making it difficult for DO to effectively restrict the SERVER’s access to their data or control which DU is authorized to retrieve the data. Consequently, malicious SERVERs or unauthorized DU may infringe upon the privacy rights of DO. This paper presents SecOutPIR, a novel outsourced PIR system that addresses two key challenges: privacy preservation for DO and access control for DU. SecOutPIR integrates attribute-based encryption for fine-grained retrieval access control to ensure that only DU with valid retrieval can access the data, while also utilizing a decentralized identity management system based on decentralized identifiers and verifiable credentials to authenticate DU requests. The proposed system ensures that the DO’s data privacy is protected during data storage and retrieval, while also ensuring that only DU with authorized retrieval can make retrieval requests, thus preventing unauthorized access. We provide a detailed description of the system model, security requirements, and an in-depth security analysis. Furthermore, experimental results demonstrate that SecOutPIR significantly enhances the practicality and efficiency of PIR in outsourced settings by enabling fine-grained retrieval access control without degrading query performance. Our implementation demonstrates that the SERVER reply time increases with the dataset size, from 82.5 ms (1000 entries) to 113.8 ms (2000 entries) and 199.6 ms (5000 entries), while the query generation time remains approximately constant at around 2.0 ms.

Accurate and real-time location tracking is vital for various applications in public safety and the military, particularly in search and rescue missions. Traditional filtering localization algorithms are more effective in linear environments and require precise initial estimates and system noise for optimal results. In complex and unreliable environments, these algorithms often yield poor localization results. To address these issues, this paper proposes a multi-agent collaborative localization algorithm based on reinforcement learning compensation filtering to tackle localization problems in complex environments and improve the robustness and accuracy of the localization algorithm. Specifically, this paper introduces a value decomposition-based reinforcement learning network for filtering compensation to reduce overall localization error and address the credit allocation problem in multi-agent reinforcement learning. This approach reduces the system’s positioning errors and addresses credit allocation issues common in multi-agent reinforcement learning.

Interrupt-driven programs are widely used in safety-critical fields like aerospace and embedded systems. However, the unpredictable interleaving of Interrupt Service Routines (ISRs) can lead to concurrency bugs, particularly atomicity violations when ISRs preempt atomic sequences of instructions. To address this, we propose a dynamic approach for detecting atomicity violations in interrupt-driven programs. Extensive experiments demonstrate that our method is more precise and efficient than related approaches.

Metaverse has emerged as a revolutionary technique for transforming the way people interact with digital content, which relies on a distributed computing and communication infrastructure, encompassing terminal users, edge servers, and cloud servers. However, the rapid evolution of the Metaverse presents challenges that surpass the capabilities of existing communication and network infrastructures, particularly on network bandwidth and latency. Additionally, human experience becomes a critical factor in this domain. Therefore, we introduce a human-aware hierarchical software defined network (SDN) architecture consisting of a Metaverse cloud layer, a mobile edge computing (MEC) server empowered edge layer, and a distributed terminal layer. Each MEC server dynamically controls a multi-antenna base station (BS) and several reconfigurable intelligent surfaces (RISs) according to the terminal immersive experience requirements in real-time. To overcome the bandwidth limitation, we propose a novel smart reconfigurable spatial reuse new radio in unlicensed spectrum (NR-U) framework, which can realize customizable communications through flexibly and coordinately reconfiguring beams among the coordination between BSs and RISs. The objective function is formulated as a Lyapunov optimization based decentralized partially-observable Markov decision process (Dec-POMDP) problem to maximize the spectral efficiency while guaranteeing the latency and reliability requirements in Metaverse, via a joint user selection, phase-shift control, and beam coordination strategy. To solve the above non-convex, strongly coupled, and mixed integer nonlinear programming (MINLP), we propose a novel multi-agent hierarchical deep reinforcement learning (MAHDRL) algorithm that integrates deep Q-network (DQN) to solve discrete problems, deep deterministic policy gradient (DDPG) to solve continuous problems, and mixing network to capture complex interactions between multiple agents. Numerical results demonstrate the effectiveness of the proposed algorithm and verify the performance improvements compared to traditional multi-agent deep reinforcement learning (MADRL) algorithms.

In order to well solve the problem of the output voltage unbalance, which is caused by unbalanced load of high frequency link matrix converter(MC), a kind of high frequency link three-phase four-leg MC has been proposed. Firstly, the root cause of the output voltage unbalance is analyzed. Then through building up the mathematical model of high frequency link three-phase four-leg MC, it is verified in theory that the de-re-couple control strategy is suitable for three-phase four-leg MC. Finally, closed-loop system suiting for a variety of unbalanced loads is simulated by MATLAB software based on the symmetrical component. The simulation results show that the high frequency link three-phase four-leg MC is a good solution to imbalanced output voltage.

After analyzing the specific security requirements of the wireless communication network based on IEEE802.16, a series of key issues about security methods and techniques were researched. A new authentication protocol for wireless mobile environment was proposed and designed. Then, its security was analyzed from the angle of technical realization and formal analysis to verify whether the original safety goals were achieved. Especially, formal analysis was mostly done. Finally, system testing was also carried out and the results were analyzed to verify function and performance of the new protocol.