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.

Integrating natural language processing (NLP) with generative pre-trained transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the patient's well-being. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, it often faces problems such as path explosion, which seriously affects efficiency. Although several studies have been conducted to reduce the number of computational paths, this problem remains a significant obstacle. Therefore, more efficient solutions are urgently needed to ensure software security. This paper proposes a large-scale language model (LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software.

With the rapid development of smart device technology, the current version of the Internet of Things (IoT) is moving towards a multimedia IoT because of multimedia data. This innovative concept seamlessly integrates multimedia data with the IoT-Edge Continuum. Recently, a distributed learning framework has shown promise in revolutionizing various industries, including smart cities, healthcare, etc. However, these applications may face challenges, such as the presence of malicious devices that invade the privacy of other devices or corrupt uploaded model parameters. Additionally, the existing synchronous federated learning (FL) methods face challenges in effectively training models on local datasets due to the diversity of IoT devices. To tackle these concerns, we propose an efficient and privacy-enhanced asynchronous FL approach for multimedia data in edge-based IoT. In contrast to traditional FL methods, our approach combines revocable attribute-based encryption (RABE) and differential privacy (DP). This guarantees the privacy of the entire process while allowing seamless collaboration between multiple devices and the aggregation server during model training. Also, this combination brings a dynamic nature to the system. Furthermore, we utilize an asynchronous weight-based aggregation algorithm to improve the efficiency of training and the quality of the final returned model. Our proposed scheme is confirmed by theoretical safety proofs and experimental results with multimedia data. Performance evaluation shows that our framework reduces the cryptography runtime by 63.3% and the global model aggregation time by 61.9% compared to cutting-edge schemes. Moreover, our accuracy is comparable to the most primitive FL schemes, maintaining 86.7%, 70.8%, and 86.1% on MNIST, CIFAR-10, and Fashion-MNIST, respectively. The experimental results highlight the remarkable practicality, resilience and effectiveness of the proposed scheme.

The automotive industry is rapidly moving toward new energy, intelligence, and connectivity, driven by policies, technological advances, and growing environmental awareness, which are pushing more people to adopt electric vehicles (EVs). Moreover, EVs are driven as distributed energy storage units by Vehicle-to-Grid (V2G), which facilitates a two-way exchange of energy. The grid and vehicles can change the power. By managing when and how EVs charge, V2G helps organize the charging process, making EVs more flexible and valuable energy resources. However, a major challenge in V2G is the authentication between EVs and charging stations (CSs), as attackers may impersonate legitimate EVs to gain unauthorized access, wasting resources and harming real users. Current authentication methods also suffer from weaknesses in key security and user privacy. In this paper, a novel method based on Cryptographic Reverse Firewalls (CRFs) is proposed, which uses random numbers to ensure the security of the private key and prevent leakage and malicious tampering. This makes it difficult for attackers to steal original keys. Moreover, the proposed scheme can conceal the relationship between signatures and vehicle owners, thereby ensuring user anonymity. Security analysis shows that the proposed scheme can resist spoofing attacks, prevent signature forgery, and ensure secure key management. Besides, experimental results confirm that the scheme is efficient, making it suitable for practical V2G applications.

With the rapid development of technology, smart environments utilizing the Internet of Things, artificial intelligence, and big data are improving the quality of life and work efficiency through connected devices. However, these advances present significant security challenges. The data generated by these smart devices contains many private and sensitive information. In data transmission, crime and terrorism may intercept this sensitive information and use it for secret communications and illegal activities. Steganography hides information in media files and prevents information leakage and interception by criminal and terrorist networks in an intelligent environment. It is an important technology to protect data integrity and security. Traditional steganography techniques often cause detectable distortions, whereas Steganography Without Embedding (SWE) avoids direct modification of cover media, thereby minimizing detection risks. This paper introduces an innovative and robust technique called Robust Linked List (RLL)-SWE, which improves resistance to attacks compared to traditional methods. Using multiple median downsampling and gradient calculations, this method extracts stable features. It restructures them into a multi-head unidirectional linked list, ensuring accurate message retrieval and high resistance to adversarial attacks. Comprehensive analysis and simulation experiments confirm the technique’s exceptional effectiveness and steganographic capacity.

Abstract The next generation of Internet of Things (IoT) networks and mobile communications (5G IoT networks) has the particularity of being heterogeneous, therefore, it has very strong ability to compute, store, etc. Group-oriented applications demonstrate its potential ability in 5G IoT networks. One of the main challenges for secure group-oriented applications (SGA) in 5G IoT networks is how to secure communication and computation among these heterogeneous devices. Conventional protocols are not suitable for SGA in 5G IoT networks since multiparty joint computation in this environment requires lightweight communication and computation overhead. Furthermore, the primary task of SGA is to securely transmit various types of jointly computing data. Hence, membership authentication and secure multiparty joint arithmetic computation become two fundamental security services in SGA for 5G IoT networks. The membership authentication allows communication entities to authenticate their communication partners and the multiparty joint computations allow a secret output to be shared among all communication entities. The multiparty joint computation result can be used to protect exchange information in the communication or be used as a result that all users jointly compute by using their secret inputs. A novel construction of computation/communications-efficient membership authenticated joint arithmetic computation is proposed in this paper for 5G IoT networks, which not only integrates the function of membership authentication and joint arithmetic computation but also realizes both computation and communication efficiency on each group member side. Our protocol is secure against inside attackers and outside attackers, and also meets all the described security goals. Meanwhile, in this construction the privacy of tokens can be well protected so tokens can be reused multiple times. This proposal is noninteractive and can be easily extended to joint arithmetic computation with any number of inputs. Hence, our design has more attraction for lightweight membership authenticated joint arithmetic computation in 5G IoT networks.