The rapid proliferation of IoT devices has popularized crowdsensing for distributed data collection, where Truth Discovery plays a critical role in inferring reliable information from heterogeneous observations. However, existing privacy-preserving truth discovery schemes face challenges including inefficient verifiability, limited weighting strategies, insufficient robustness, and excessive overhead. In this paper, we introduce VRPMTD, a verifiable, robust, and privacy-preserving multi-dimensional truth discovery framework. We achieve scalable verifiability via CRT based packing of multidimensional measurements together with commitments and a linear homomorphic hash. This design allows the data requester to batch verify aggregated results. To improve accuracy, we design a novel weighting mechanism using a Gaussian radial basis function residual and a sliding-window temporal loss, allowing workers’ weights to reflect both long-term reliability and recent behavior. Additionally, the framework improves robustness under realistic sensing and network failures. To optimize efficiency, we implement a lightweight dual-layer encryption mechanism and a difference-based uploading strategy. Formal security analysis indicates that VRPMTD preserves the input-level confidentiality of workers’ raw measurements and ensures verifiability of outsourced aggregation. Extensive experiments on real-world datasets and IoT devices demonstrate that VRPMTD achieves higher accuracy while incurring lower overhead.

The convergence of edge intelligence and networked medical infrastructures in the Artificial Intelligence of Medical Things (AIoMT) is transforming healthcare toward personalization and predictive intervention. In this paradigm, high-resolution physiological data continuously flow between wearable or implantable devices, edge nodes, and cloud analytics platforms. Such connectivity enables advanced diagnostic modeling and real-time decision support. However, it also enlarges the attack surface. AIoMT components are exposed to impersonation, replay, and man-in-the-middle attacks. Therefore, secure data exchange becomes essential. Authentication and key exchange (AKE) schemes address this requirement by enabling mutual authentication and session key establishment over insecure channels. Nevertheless, many existing centralized designs suffer from single points of failure and insider threats. Several blockchain-assisted approaches still retain centralized identity traceability. In addition, most AKE schemes either neglect physical security, lack tolerance to intrinsic physical unclonable function (PUF) noise, or store sensitive PUF challenge–response pairs, which increases the risk of modeling attacks. To address these issues, we propose a fully decentralized authentication and key exchange scheme (FDAKES) for AIoMT. FDAKES adopts a$(t,n)$threshold-based root of trust across multiple registration centers (MRCs) to remove unilateral control in registration and tracing. Its server-independent AKE process combines threshold-protected identities, dynamic nonces, timestamps, and PUF and biometric enhanced credentials to achieve perfect forward secrecy. Decentralized conditional traceability preserves user anonymity while allowing identity recovery only with unanimous MRCs consent. By integrating PUF with a fuzzy extractor, FDAKES enables stable secret regeneration without storing raw challenge–response pairs, thereby mitigating modeling threats. We formally prove protocol correctness for login authentication and mutual key agreement. We further establish semantic security of the session key under the real-or-random model, showing that the adversary advantage is negligible in the random oracle model. An extensive informal analysis demonstrates resistance to impersonation, replay, guessing, modeling, physical, man-in-the-middle, and key compromise attacks. Experimental evaluation demonstrates that FDAKES reduces total computational overhead by up to 40.95% and at least 17.25% percent compared with recent state-of-the-art AKE schemes, while communication cost is reduced by up to 76.73% and at least 5% across representative baselines. This work establishes a robust and fully decentralized trust foundation for next-generation smart healthcare systems.

In response to critical security threats such as data tampering, identity impersonation, and channel eavesdropping in Vehicle-to-Grid (V2G) networks, numerous charging authentication schemes have been proposed. However, these schemes either lack sufficient anonymity, physical security, or electricity consumption data aggregation for electricity dispatch. In light of these considerations, we propose a comprehensive solution—a physically secure and privacy-preserving charging authentication framework with data aggregation, comprising two foundational schemes. The first scheme introduces a fully anonymous authentication system. In this approach, an Electric Vehicle (EV) seeking charging generates a random signature for its charging request. Subsequently, a Charging Station (CS) verifies the signature, granting charging services upon successful validation. Notably, this process guarantees the EV’s real identity remains undisclosed, even to the Control Center (CC). Moreover, this scheme also addresses potential physical attacks through the incorporation of a physical unclonable function. The second scheme involves a privacy-preserving data aggregation scheme, aggregating total electricity consumption of CSs in a given area while simultaneously preserving individual CSs’ electricity consumption data from potential leakage. Subsequently, the aggregated electricity consumption data is transmitted back to the CC, enabling efficient electricity coordination. A detailed security and privacy analysis demonstrates that our proposed framework meets intended security and privacy objectives. The final performance evaluation underscores the advantages of our proposed framework in comparison with related work.

An increasing number of researchers are turning their attention to signcryption, particularly in the context of multi-receiver communication scenarios, due to its ability to simultaneously provide authentication, integrity, and confidentiality of messages. However, existing signcryption schemes have not been able to fully implement sender unlinkability. Specifically, when a sender signcrypts a secret message and obtains the corresponding ciphertext, the intended recipient must use the sender’s identity or public key to complete the unsigncryption process and retrieve the plaintext. Consequently, the recipient can link the sender via the same identity or public key. To address this issue, we present an Unlinkable Signcryption Scheme for Multi-Receiver (USS-MR). With Chinese Remainder Theorem (CRT), our USS-MR enables a vehicle to send the same secret message to a group of RoadSide Units (RSUs). Additionally, when a new message requires signcryption, the vehicle generates a new key pair, making it impossible for any RSU to link the vehicle through its public key. In our USS-MR, we have adopted a pseudonym mechanism to provide conditional privacy, which hides the real identity of the vehicle through the use of pseudonyms and avoids linking it to the identity. Moreover, if a vehicle is found to engage in malicious behavior, it will not only be tracked but also subjected to revocation. Comprehensive security analyses demonstrate that our USS-MR satisfies various security, privacy, and functionality requirements and effectively resists common attacks in Vehicular Ad-hoc Networks (VANETs). Finally, our USS-MR demonstrates certain advantages in terms of computation and communication when compared to relevant studies. In particular, our USS-MR maintains a consistent communication burden of 388 bytes.

Route planning helps a vehicle to share a message with the roadside units (RSUs) on its path in advance, which greatly speeds the authentication between the vehicle and the RSUs when the vehicle enters the RSUs’ coverage. In addition, since only a small amount of necessary information needs to be shared between the vehicle and the RSUs, route planning can reduce the storage overhead of the vehicle’s on-board unit (OBU) and the RSUs. However, the message sharing requires the assistance of the certification authority (CA), which will lead CA easily to obtain the vehicle’s planning route. Although CA knows the vehicle’s registration information and helps the vehicle to communicate with RSUs, it is unacceptable that the path of their vehicle is obtained by CA for most drivers. In fact, vehicle’s sensitive information such as planning route, starting time, stop place, should be privacy for others including CA. Inspired with the method of oblivious transfer, a preserving-privacy route planning scheme in VANETs is proposed in this article, in which, a vehicle deduces the information of RSUs on its path with the help of CA, while CA knows nothing about which RSUs’ information has been deduced by the vehicle. Later, fast authentication or other service is easily achieved between the vehicle and the RSUs (V2R) with the pre-shared information. After V2R authentication, vehicles could easily communicate with adjacent vehicles with the help of RSUs (V2V). Finally, compared with related schemes, performance evaluation illustrates the proposed scheme is better in terms of time consumption.