Biblio

Deep learning-based semantic communications (DLSC) significantly improve communication efficiency by only transmitting the meaning of the data rather than a raw message. Such a novel paradigm can brace the high-demand applications with massive data transmission and connectivities, such as automatic driving and internet-of-things. However, DLSC are also highly vulnerable to various attacks, such as eavesdropping, surveillance, and spoofing, due to the openness of wireless channels and the fragility of neural models. To tackle this problem, we present SemKey, a novel physical layer key generation (PKG) scheme that aims to secure the DLSC by exploring the underlying randomness of deep learning-based semantic communication systems. To boost the generation rate of the secret key, we introduce a reconfigurable intelligent surface (RIS) and tune its elements with the randomness of semantic drifts between a transmitter and a receiver. Precisely, we first extract the random features of the semantic communication system to form the randomly varying switch sequence of the RIS-assisted channel and then employ the parallel factor-based channel detection method to perform the channel detection under RIS assistance. Experimental results show that our proposed SemKey significantly improves the secret key generation rate, potentially paving the way for physical layer security for DLSC.

Mobile edge computing (MEC) emerges recently to help process the computation-intensive and delay-sensitive applications of resource limited mobile devices in support of MEC servers. Due to the wireless offloading, MEC faces many security challenges, like eavesdropping and privacy leakage. The anti-eavesdropping offloading or privacy preserving offloading have been studied in existing researches. However, both eavesdropping and privacy leakage may happen in the meantime in practice. In this paper, we propose a privacy preserved secure offloading scheme aiming to minimize the energy consumption, where the location privacy, usage pattern privacy and secure transmission against the eavesdropper are jointly considered. We formulate this problem as a constrained Markov decision process (CMDP) with the constraints of secure offloading rate and pre-specified privacy level, and solve it with reinforcement learning (RL). It can be concluded from the simulation that this scheme can save the energy consumption as well as improve the privacy level and security of the mobile device compared with the benchmark scheme.

The time-varying properties of the wireless channel are a powerful source of information that can complement and enhance traditional security mechanisms. Therefore, we propose a cross-layer authentication mechanism that combines physical layer channel information and traditional authentication mechanism in LTE. To verify the feasibility of the proposed mechanism, we build a cross-layer authentication system that extracts the phase shift information of a typical UE and use the ensemble learning method to train the fingerprint map based on OAI LTE. Experimental results show that our cross-layer authentication mechanism can effectively prompt the security of LTE system.

This paper investigates the problem of generating two secret keys (SKs) simultaneously over a five-terminal system with terminals labelled as 1, 2, 3, 4 and 5. Each of terminal 2 and terminal 3 wishes to generate an SK with terminal 1 over a public channel wiretapped by a passive eavesdropper. Terminal 4 and terminal 5 respectively act as a trusted helper and an untrusted helper to assist the SK generation. All the terminals observe correlated source sequences from discrete memoryless sources (DMS) and can exchange information over a public channel with no rate constraint that the eavesdropper has access to. Based on the considered model, key capacity region is fully characterized and a source coding scheme that can achieve the capacity region is provided. Furthermore, expression for key leakage rate is obtained to analyze the security performance of the two generated keys.

Delivery service via ridesharing is a promising service to share travel costs and improve vehicle occupancy. Existing ridesharing systems require participating vehicles to periodically report individual private information (e.g., identity and location) to a central controller, which is a potential central point of failure, resulting in possible data leakage or tampering in case of controller break down or under attack. In this paper, we propose a Blockchain secured ridesharing delivery system, where the immutability and distributed architecture of the Blockchain can effectively prevent data tampering. However, such tamper-resistance property comes at the cost of a long confirmation delay caused by the consensus process. A Hash-oriented Practical Byzantine Fault Tolerance (PBFT) based consensus algorithm is proposed to improve the Blockchain efficiency and reduce the transaction confirmation delay from 10 minutes to 15 seconds. The Hash-oriented PBFT effectively avoids the double-spending attack and Sybil attack. Security analysis and simulation results demonstrate that the proposed Blockchain secured ridesharing delivery system offers strong security guarantees and satisfies the quality of delivery service in terms of confirmation delay and transaction throughput.