With the proliferation of electric vehicles (EVs), private charging pile (PCP) sharing networks are likely to be an integral part of future smart cities, especially in places with limited public charging infrastructure. However, there are a number of operational challenges associated with the deployment of PCPs in such a shared and untrusted environment. For example, how do we formulate efficient PCP sharing strategies in PCP sharing networks, while also taking into consideration the dynamic charging behaviors of EVs? Therefore, in this paper, we propose an energy b lockchain- b ased secure P C P sharing scheme (BBC) for PCP sharing networks. First, an energy blockchain-based framework is designed for PCP sharing networks to facilitate energy sharing services for EVs and PCPs, using both distributed ledgers and cryptocurrency. Then, we devise a reputation-based secure PCP sharing algorithm to improve consensus efficiency with smaller signature sizes. In addition, a distributed reputation mechanism is constructed to assess the trustworthiness of consensus nodes in blockchain, based on ratings, behaviors, and fading. We also model the interactions among EVs and cooperative PCPs as a joint coalition-matching game, and obtain the optimal strategies of PCPs and EVs by analyzing the Nash-stable coalitional structure and stable many-to-one matching pairs. Extensive simulations and the real-world implementation demonstrate that the proposed approach improves the utility of EV users and renewable energy efficiency in PCP sharing networks.

The conditional privacy-preserving authentication (CPPA) protocol has applications in the construction of secure Internet of Vehicles (IoV) due to its capability to achieve both privacy preservation and authentication simultaneously. We demonstrate the challenge in designing secure CPPA protocols, e.g., the insecurity of master key, invalidity of pseudo-identity (PID), linkability etc. Then, we present a certificate-less CPPA (CL-CPPA) protocol to be used in vehicular environments that supports both privacy and security requirements in the IoV system, where the vehicles and Trusted Authority (TA) does not need to store any certificates for verification and tracking, respectively. We also demonstrate that our proposed CL-CPPA protocol is secure against modification attacks, impersonation attacks, and other existing attacks. A comparative summary shows that our proposed CL-CPPA protocol has lower computation and communication costs in comparison to the state-of-the-art protocols.

Designing efficient and secure mutual authentication and key agreement (MAAKA) protocols for Internet of Medical Things (IoMT) has been shown to be challenging, mainly due to the different security and privacy requirements in complex settings. Existing schemes generally are subject to a number of limitations, ranging from performance to security issues. In this article, we introduce a provably secure and lightweight MAAKA (PSL-MAAKA) protocol for fully public channels in IoMT. First, the proposed scheme is lightweight since the major operations in the stage of authentication and key agreement are hash operation and XOR operation, respectively. Second, this article proves the security of the presented protocol taking the advantage of the random oracle model. Next, this article gives that security requirements in IoMT could be satisfied through our presented MAAKA protocol. Finally, we demonstrate that it enjoys optimal performance than other competing schemes, in terms of communication overhead, computation overhead, and storage overhead.

Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)’ computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs’ task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving off-chain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles’ bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV’s energy costs and expenses with low overheads.

The rise of advanced persistent threats (APTs) has marked a significant cybersecurity challenge, characterized by sophisticated orchestration, stealthy execution, extended persistence, and targeting valuable assets across diverse sectors. Provenance graph-based kernel-level auditing has emerged as a promising approach to enhance visibility and traceability within intricate network environments. However, it still faces challenges including reconstructing complex lateral attack chains, detecting dynamic evasion behaviors, and defending smart adversarial subgraphs. To bridge the research gap, this paper proposes an efficient and robust APT defense scheme leveraging provenance graphs, including a network-level distributed audit model for cost-effective lateral attack reconstruction, a trust-oriented APT evasion behavior detection strategy, and a hidden Markov model based adversarial subgraph defense approach. Through prototype implementation and extensive experiments, we validate the effectiveness of our system. Lastly, crucial open research directions are outlined in this emerging field.