This paper investigates an energy-constrained, re-configurable intelligent surface (RIS)-assisted framework that integrates sensing and covert backscatter communication through a stagewise feedback mechanism in the presence of masquerading eavesdroppers located in blind zones. The system operates in two stages on a unified hardware platform. In the sensing stage, the base station cooperates with a terminal-side semi-passive RIS to synthesize directional beampatterns and establish virtual line-of-sight paths for improved angular observability. In the communication stage, the RIS switches to low-power backscatter to deliver data while steering nulls toward suspicious directions inferred from sensing. A stagewise feedback mechanism links the two stages: a KL divergence-based covert requirement is translated into a directional leakage-power budget and then mapped to a Cramér-Rao-style bound sensing-accuracy target for the next round. The coupled waveform designs are formulated for both stages, and an alternating optimization framework with majorization-minimization and convex relaxations (SOCP/SDP subproblems under unit-modulus RIS constraints) is developed. Simulation results show monotonic outer-loop improvement of the legitimate link SNR with the worst-case leakage consistently below the covert cap; inner loops typically converge within a few steps. Flat-top mainlobe shaping further reduces the sensing bound and enhances robustness. Compared with a no-feedback variant, the proposed scheme achieves higher rates under stringent covert requirements, while an optional safety gate that pauses transmission in near-collinear cases preserves feasibility. These results demonstrate that sensing-driven feedback enables energy-efficient covert communication with improved resolution and link quality.

Triangle counting serves as a foundational operator in graph analysis. Since graph data often contain sensitive information about entities, the release of triangle counts poses privacy concerns. While recent studies have addressed privacy-preserving triangle counting, they mainly concentrate on unsigned graphs. In this paper, we investigate a new problem of developing triangle counting algorithms for signed graphs that adhere to centralized differential privacy and local differential privacy, respectively. The inclusion of edge signs and more classes of triangles leads to increased complexity and overwhelms the statistics with noise. To overcome these problems, we first propose a novel algorithm for smooth-sensitivity computation to achieve differential privacy under the centralized model. In addition, to handle large signed graphs, we devise a computationally efficient function that calculates a smooth upper bound on local sensitivity. Finally, we release the approximate triangle counts after the introduction of Laplace noise, which is calibrated to the smooth upper bound on local sensitivity. In the local model, we propose a two-phase framework tailored for balanced and unbalanced triangle counting. The first phase utilizes the Generalized Randomized Response mechanism to perturb data, followed by a novel response mechanism in the second phase. Extensive experiments conducted over real-world datasets demonstrate that our proposed methods can achieve an excellent trade-off between privacy and utility.

Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach inherently involves large-scale distributed graph processing, which closely aligns with the challenges and research focuses of graph-based data systems. Despite the proliferation of FGL, the diverse motivations from real-world applications, spanning various research backgrounds and settings, pose a significant challenge to fair evaluation. To fill this gap, we propose OpenFGL, a unified benchmark designed for the primary FGL scenarios: Graph-FL and Subgraph-FL. Specifically, OpenFGL includes 42 graph datasets from 18 application domains, 8 federated data simulation strategies that emphasize different graph properties, and 5 graph-based downstream tasks. Additionally, it offers 18 recently proposed SOTA FGL algorithms through a user-friendly API, enabling a thorough comparison and comprehensive evaluation of their effectiveness, robustness, and efficiency. Our empirical results demonstrate the capabilities of FGL while also highlighting its potential limitations, providing valuable insights for future research in this growing field, particularly in fostering greater interdisciplinary collaboration between FGL and data systems.

To solve the profit allocation problem among multiagent energy systems (MAESs), this study proposes a cooperative operation strategy for the MAES integrated with wind, hydrogen, and buildings based on the asymmetric Nash bargaining (NB) approach. The modeling of the MAES, including wind farms, hydrogen systems, and buildings is conducted, with detailed electrical and thermal characteristics used to improve operation flexibility while ensuring the users’ comfort. To solve the issue of equal profit allocation in symmetric NB, this article proposes using asymmetric NB to achieve a rational profit allocation. Functions are constructed to quantify the agents’ contributions, which are then used as bargaining power in the asymmetric NB process to realize an asymmetric profit allocation. Furthermore, the proposed distributed algorithm three-stage predictor-corrector accelerated alternating direction method of multipliers (ADMM) algorithm can improve solving efficiency and preserve information privacy. The case studies show that compared with equal profit allocation by symmetric NB, the proposed asymmetric NB-based strategy can acknowledge each agent's contribution and allocate agents with profits that correspond to their respective contributions. The fairness and rationality of the asymmetric profit allocation method can benefit the cooperative relationships among agents.

The multi-agent characteristics of integrated energy systems are becoming increasingly prominent, rendering the energy management problems more intricate. Additionally, the distributed agents are faced with challenges from the communication layer, such as packet losses and privacy disclosure issues. Therefore, a privacy-preserving neurodynamic-based optimization strategy considering packet losses is proposed in this article. A privacy-preserving communication model is constructed based on differential privacy (DP). Unlike traditional DP methods, the privacy preservation mechanism employed in this article can achieve a quantified high level of privacy while ensuring the solution accuracy of the optimization algorithm. Moreover, communication packet loss models based on both the two-state Markov process and the Bernoulli process are established. The proposed fully distributed scheme only requires communication between adjacent agents. The efficiency of the neurodynamic-based approach, the high-level privacy preservation, and the robustness against communication packet loss are demonstrated through several case studies.

Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks, serious privacy concerns arise when the graph data contains personal or sensitive information. To address this issue, we investigate and develop graph embedding algorithms that satisfy local differential privacy (LDP). We introduce a novel privacy-preserving graph embedding framework, named PrivGE, to protect node data privacy. Specifically, we propose an LDP mechanism to obfuscate node data and utilize personalized PageRank as the proximity measure to learn node representations. Furthermore, we provide a theoretical analysis of the privacy guarantees and utility offered by the PrivGE framework. Extensive experiments on several real-world graph datasets demonstrate that PrivGE achieves an optimal balance between privacy and utility, and significantly outperforms existing methods in node classification and link prediction tasks.

In this paper, we present an in-depth study of light emitting diode (LED) based indoor visible light communication positioning system using a smart phone camera with rolling shutter effect, aiming for smart and connected hospital applications. The LED transmits periodical signals with different frequencies as its optical tags. The camera exploits the rolling shutter effect to detect the fundamental frequency of optical signals. The roles of camera parameters determining the rolling effect are studied and a technique to measure the camera readout time per column is presented. Factors limiting the detectable optical frequency range is explained based on the discussion of rolling shutter mechanism. The Fourier spectrum based frequency resolution, which determines the tracking capacity, is analyzed.

Mixed resolution format plays an important role in 3DTV coding scheme. In recent years, several research works discussed the Super Resolution (SR) problem in Mixed resolution format, where the SR image is created using the up-sampled low resolution (LR) image and the high frequency components borrowed from the adjacent high resolution (HR) warping image. However, the quality of the obtained HR images is impaired by the misalignment error caused by depth error in 3D warping. A novel SR reconstruction model is proposed in this paper. It is composed of three components: structure term, detail information term and the regularization term. They were used to preserve the structure similarity of the LR image, to preserve the detail information from the adjacent HR image, and to ensure uniqueness of the solution respectively. Simulation results show that the proposed method achieves high performance in PSNR.