Real-time and contactless monitoring of vital signs, such as respiration and heartbeat, alongside reliable communication, is essential for modern healthcare systems, especially in remote and privacy-sensitive environments. Traditional wireless communication and sensing networks fall short in meeting all the stringent demands of eHealth, including accurate sensing, high data efficiency, and privacy preservation. To overcome the challenges, we propose a novel integrated sensing, computing, and semantic communication (ISCSC) framework. In the proposed system, a service robot utilises radar to detect patient positions and monitor their vital signs, while sending updates to the medical devices. Instead of transmitting raw physiological information, the robot computes and communicates semantically extracted health features to medical devices. This semantic processing improves data throughput and preserves the clinical relevance of the messages, while enhancing data privacy by avoiding the transmission of sensitive data. Leveraging the estimated patient locations, the robot employs an interacting multiple model (IMM) filter to actively track patient motion, thereby enabling robust beam steering for continuous and reliable monitoring. We then propose a joint optimisation of the beamforming matrices and the semantic extraction ratio, subject to computing capability and power budget constraints, with the objective of maximising both the semantic secrecy rate and sensing accuracy. Simulation results validate that the ISCSC framework achieves superior sensing accuracy, improved semantic transmission efficiency, and enhanced privacy preservation compared to conventional joint sensing and communication methods.

This paper investigates the secure resource allocation for a downlink integrated sensing and communication system with multiple legal users and potential eavesdroppers. In the considered model, the base station (BS) simultaneously transmits sensing and communication signals through beamforming design, where the sensing signals can be viewed as artificial noise to enhance the security of communication signals. To further enhance the security in the semantic layer, the semantic information is extracted from the original information before transmission. The user side can only successfully recover the received information with the help of the knowledge base shared with the BS, which is stored in advance. Our aim is to maximize the sum semantic secrecy rate of all users while maintaining the minimum quality of service for each user and guaranteeing overall sensing performance. To solve this sum semantic secrecy rate maximization problem, an iterative algorithm is proposed using the alternating optimization method. The simulation results demonstrate the superiority of the proposed algorithm in terms of secure semantic communication and reliable detection.

In this work, the problem of training the secure federated learning (FL) algorithm over a multicell wireless network is investigated. FL is indeed a learning method that can protect users’ privacy, but it has also been shown to be vulnerable to gradient leakage attacks, which can leak users’ private data. Therefore, we propose a defense method to prevent gradient leakage attacks by uploading the selected layers to attend global model updates. Moreover, we add the differential privacy (DP) noise to the model during the local training to strengthen the defense further. Furthermore, to minimize the total privacy leakage for users, we formulate an optimization problem that minimizes this leakage by jointly optimizing resource block (RB) allocation, layer selection, and DP noise. To find a locally optimal solution to this problem, we divide it into two subproblems: 1) initially solving for RB allocation and 2) layer selection using successive convex approximation (SCA) for convex approximation, followed by optimizing DP noise. The simulation results demonstrate that our proposed optimization algorithm outperforms the other two benchmarks.

This paper focuses on physical layer security issues in a multiple-user-multiple-input-multiple-output dual-functional radar-communication system (MU-MIMO-DFRC), which communicates with multiple downlink communication users (CUs) whilst detecting multiple targets, who are passive eavesdroppers that could intercept the confidential message. To establish secure integrated sensing and communication (ISAC) against numerous eavesdroppers, we use artificial noise (AN) for conventional receivers (Type-I). In the case of ISAC-dedicated receivers (Type-II), instead of AN, we employ sensing signals to confuse the eavesdroppers. We proposed secure ISAC beamforming designs for conventional and ISAC-dedicated receivers. For conventional receivers, we aim to minimise the summation of all eavesdroppers’ SNR. The optimisation problem is a fractional programming problem solved iteratively. For ISAC-dedicated receivers, we optimise the radar beam pattern towards the eavesdroppers, followed by a least-square minimisation between the ISAC beam pattern and the obtained radar beam pattern. The proposed design for ISAC-dedicated receivers is computationally efficient since the optimal solution is determined non-iteratively. The simulation results demonstrate that our design is able to find a compromise between sensing targets and communicating with the CUs while ensuring information confidentiality for both types of receivers.

In physical layer security, one interest of the community is the development of practical approaches to achieve reliable and secure communication, such as model-free approaches, in which the gradient of the channel model is required. This paper proposes a new model-free approach based on information-theoretic metrics. We train the encoder with deep reinforcement learning that uses a policy-based gradient descent algorithm whose loss function contains a feed-forward neural network. Simulation results show that our model is capable of retaining the eavesdropper’s BLER at a high level whilst ensuring the legitimate receiver’s BLER reduces to nearly zero.