The Industrial Internet of Things (IIoT) has been widely studied, which dramatically enhanced the manufacturing efficiency and service elasticity. However, how to ensure the data confidentiality and security in the event-driven publish/subscribe communication model becomes a cumbersome problem. To address this concern, ring signatures have been researched deeply. Nevertheless, existing solutions have large computational burdens and neglect to incorporate reporting and tracing features, which makes it impractical for IIoT. In this way, research focus on designing an efficient report and traceable ring signature is still far-reaching. In this article, we propose RAT ring, a novel report and traceable ring signature, which provides publisher authentication, anonymous communication, reporting, and tracing. To achieve this, we adopt the zero knowledge proof to verify the authenticity of publisher data, and the signature of knowledge to trace the signature. Then, we formalize and prove the security of our scheme. Eventually, through comprehensive performance evaluation, our scheme outperforms prior works by approximately up to 51 times in terms of total computational overhead. These results demonstrate that our design is practical and effective for data privacy-preserving in IIoT.

As the world’s population gradually enters the aging stage, the smart healthcare demand has become extremely urgent and incomparably important. Healthcare wireless sensor networks (HWSNs) is one of the foundations of smart healthcare. Medical information is extremely sensitive information. However, HWSNs uses a common communication channel and is vulnerable to various types of security attacks. Therefore, ensuring the communication and data security of HWSNs and protecting patient privacy are the basis of smart healthcare. After analyzing the deficiencies of a certificateless aggregation signature scheme for HWSNs, an improved certificateless aggregation signature scheme (called iCLAS) is proposed in this paper.We present rigorous security proof that shows iCLAS can resist all kinds of security attacks and ensure patient privacy protection. The detailed performance comparisons show iCLAS has clear advantages in computation and communication cost.

Blood smear analysis is often used to diagnose diseases like malaria, Anemia, Leukemia, etc. Morphological changes, such as size, shapes, and color, are receiving much attention in pathological analysis. Existing methods for detecting, diagnosing and analyzing blood smears cannot quantify overlapped, irregular boundaries and complex structures. This work proposes and evaluates a framework that utilizes Generative adversarial networks (GANs) for the segmentation and classification of blood elements, that is, white blood cells (WBCs), red blood cells (RBCs), and platelets (PLTs) simultaneously. The Generator of the network determines the mapping from microscopic images of blood cells to a confidence map. This mapping stipulates the probabilities of the pixel of the microscopic blood cell images with respect to ground truth. The Discriminator of the network is essential to castigate the mismatch between the microscopic blood cells images and confidence map. Additionally, adversarial learning enables the Generator to generate a qualitative confidence map that is converted into segmented images. We have calculated minimum, maximum, and average losses to judge the performance of the proposed model. We measure structural similarity, peak signal-to-noise ratio, pixel classification error, and finally, classified cells. The proposed framework can analyze all the blood cell elements simultaneously. The proposed framework shows a significant improvement in the segmentation and classification of blood cell elements compared to state-of-the-art techniques. During the training process, generator total loss reduces by 12.18%, 5.39%, and 3.62% for RBCs, WBCs, and PLTs, respectively. Our results demonstrate that the proposed framework outperforms existing state-of-the-art techniques, achieving the highest pixel correctly classified (PCC) ratio for the segmentation of blood cells as 99.8%, 93.4%, and 99.9% for WBCs, RBCs, and PLTs, respectively. Our framework attains 95.45% and 88.89% classification accuracy for WBCs on ALL-IDB-I and ALL-IDB-II datasets. The dataset used for this study can be found at https://drive.google.com/drive/folders/1F7kZ1SRWUD9R6aHLMkj3wsjcHnvlGuwP?usp=sharing.

The large amount of data collected by intelligent devices in consumer electronics cannot be fully utilized because it involves a lot of privacy information. At present, researchers propose many security protection schemes, among which the scheme using edge computing architecture attracts much attention. However, existing schemes cannot simultaneously address security, efficiency, and robustness, especially in the case of intelligent devices dropout. Therefore, we propose an intelligent device data secure federated learning scheme using edge computing architecture named ApaPRFL. ApaPRFL is based on the gradient strong privacy-preserving method using secure secret sharing. It leverages the property of high regional similarity to ensure system stability even when the end devices (intelligent devices) dropout. Additionally, it improves the efficiency of poisoning detection and reduces error rates. The performance of ApaPRFL is evaluated on two real datasets. Experimental results demonstrate that ApaPRFL is more effective in countering two typical poisoning attacks compared to similar schemes.

Electronic Health Records (EHRs) based on the Internet of Things (IoT) can provide real-time health data for quick intelligent medical services and give convenience to many data-sharing scenarios. However, EHRs also face various security threats since they are highly private. To the best of our knowledge, no recognized data-sharing work can satisfy the stringent privacy requirements of EHRs. Motivated by this, we propose a blockchain-based personalized access control EHR sharing scheme with data verifiability, which can safeguard the interests of data owners and users simultaneously. Firstly, we take ciphertext-policy attribute-based encryption to achieve personalized access control for data owners. Secondly, we design an interactive zero-knowledge proof protocol between data owners and users, which can provide authenticity verification of EHR for users and prevent EHR away from forgery. In addition, smart contracts and the interplanetary file system are used to reduce the computation and storage costs of patients. Finally, the security analysis shows that the proposed scheme meets the pre-defined security goals. The performance analysis demonstrates that the proposed scheme is efficient, and can be applied to practical electronic medical record sharing scenarios.