In blockchain applications, ring signatures offer significant advantages, particularly in decentralized settings, safeguarding user privacy and data security. This paper presents two innovative ring signature schemes to address the space performance challenges arising from the widespread use of ring signatures in blockchain transactions. Firstly, we introduce an aggregated ring signature scheme that effectively improves the signature space from $$\mathcal {O}(m\log n)$$ to $$\mathcal {O}(\log mn)$$ and provides corresponding security proofs. Secondly, we propose a compact multi-message ring signature scheme based on the combination lock principle, enhancing signature space efficiency to $$\mathcal {O}(m+n)$$ and optimally up to $$\mathcal {O}(m+\log n)$$ . These schemes exhibit outstanding performance in privacy-centric blockchain transactions, as demonstrated through performance analysis in practical scenarios. Additionally, we introduce aggregated linkable tags, which maintain double-spending detection in blockchain transactions. These innovative solutions are poised to effectively tackle the space efficiency challenges associated with ring signatures in blockchain transactions, thereby providing robust support for privacy preservation and data security.

Personalized Human Activity Recognition (PHAR) based on wearable sensors is crucial in the medical, sports, industrial and other fields. PHAR faces challenges of privacy leakage and a shortage of labeled data. Therefore, we propose a framework called self-supervised federated learning for personalized human activity recognition (SSF-HAR) to implement private PHAR. To protect user privacy, our framework integrates federated learning (FL) to achieve the transmission of only model parameters between the cloud and clients, rather than user data. Besides, we propose a strategy of weighted aggregation to update the cloud model with the client models. To overcome the lack of labeled data, our framework introduces self-supervised learning (SSL) tasks to pretrain a feature extractor in the cloud. The proxy task of SSL transforms data and provides pseudo-labels in three forms. We test the performance on the benchmark datasets MotionSense and WIDSM. The experiments show that SSF-HAR outperforms other FL frameworks for PHAR.

Abstract Cervical cancer is a major health concern, particularly in developing countries with limited medical resources. This study introduces two models aimed at improving cervical tumor segmentation: a semi‐automatic model that fine‐tunes the Segment Anything Model (SAM) and a fully automated model designed for efficiency. Evaluations were conducted using a dataset of 8586 magnetic resonance imaging (MRI) slices, where the semi‐automatic model achieved a Dice Similarity Coefficient (DSC) of 0.9097, demonstrating high accuracy. The fully automated model also performed robustly with a DSC of 0.8526, outperforming existing methods. These models offer significant potential to enhance cervical cancer diagnosis and treatment, especially in resource‐limited settings.

Sensor-based Human Activity Recognition (HAR) is widely used in daily life and is the basic-level bridge to virtual healthcare in the metaverse. The current challenge is the low recognition accuracy for personalized users on smart wearable devices. The limited resource cannot support large deep learning models updated locally. Besides, integrating and transmitting sensor data to the cloud would reduce the efficiency. Considering the tradeoff between performance and complexity, we propose a Lightweight Human Activity Recognition (LHAR) framework. In LHAR, we combine the cross-people HAR task with the lightweight model task. LHAR framework is designed on the teacher-student architecture and the student network consists of multiple depthwise separable convolution layers to achieve fewer parameters. The dark knowledge distilled from the complex teacher model enhances the generalization ability of LHAR. To achieve effective knowledge distillation, we propose two optimization methods. Firstly, we train the teacher model by ensemble learning to promote teacher performance. Secondly, a multi-channel data augmentation method is proposed for the diversity of the dataset, which is a plug-in operation for the ensemble teacher model. In the experiments, we compare LHAR with state-of-art models in comparison evaluation, ablation study and the hyperparameter analysis, which proves the better performance of LHAR in efficiency and effectiveness.

Verifiable secure aggregation (VSA) is a critical procedure in federated learning (FL), where secure aggregation achieves local gradients aggregation while data confidentiality is preserved, and verifiability enables participants to verify the correctness of aggregated results returned by a central server (CS). Most of existing solutions for VSA employ cumbersome cryptographic primitives and techniques (e.g., homomorphic encryption, bilinear pairing, interactive proof systems), which impose high communication round complexity and computational costs on participants or CS. Besides, user dropouts occur commonly in cross-device FL as a result of unstable network connection, it is demanded to design particular mechanism to deal with such events. In this paper, we present a robust secure aggregation scheme with lightweight verification for FL, by utilizing Shamir’s secret sharing technique to design a random masking code to protect the confidentiality of local gradients and achieve resilience to possible user dropout. To support verifiability upon aggregation, we extend a multi-key homomorphic MAC to achieve verification over gradient vector space. We provide security analysis to show that our scheme can protect data confidentiality against collusion attacks, meanwhile ensure the verifiable results are unforgeable under the assumption pseudorandom functions exist. We implement our scheme to verify its correctness and feasibility, performance evaluation shows its advantages in terms of efficiency and functionality.

Privacy protection is a hot topic in network security, many scholars are committed to evaluating privacy information disclosure by quantifying privacy, thereby protecting privacy and preventing telecommunications fraud. However, in the process of quantitative privacy, few people consider the reasoning relationship between privacy information, which leads to the underestimation of privacy disclosure and privacy disclosure caused by malicious reasoning. This paper completes an experiment on privacy information disclosure in the real world based on WordNet ontology .According to a privacy measurement algorithm, this experiment calculates the privacy disclosure of public figures in different fields, and conducts horizontal and vertical analysis to obtain different privacy disclosure characteristics. The experiment not only shows the situation of privacy disclosure, but also gives suggestions and method to reduce privacy disclosure.

The continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) deficiencies in corn fields. Current methods of field surveillance are either completed manually or with the assistance of satellite imaging, which offer infrequent and costly information to the farmers about the state of their fields. The proposed methodology promotes the use of small-scale Unmanned Aerial Vehicles (UAVs) and Computer Vision algorithms that operate with information in the visual (RGB) spectrum. Through this implementation, a lower cost solution for identifying N deficiencies is promoted. We provide extensive results on the use of commercial RGB sensors for delivering the essential information to farmers regarding the condition of their field, targeting the reduction of N fertilizers and the increase of the crop performance. Data is first collected by a UAV that hovers over a stressed area and collects high resolution RGB images at a low altitude. A recommendation algorithm identifies potential segments of the images that are candidates exhibiting N deficiency. Based on the feedback from experts in the area a training set is constructed utilizing the initial suggestions of the recommendation algorithm. Supervised learning methods are then used to characterize crop leaves that exhibit signs of N deficiency. The performance of 84.2% strongly supports the potential of this scheme to identify N-deficient leaves even in the case of images where the unhealthy leaves are heavily occluded by other healthy or stressed leaves.

The rapid growth of scientific literature calls for automatic and efficient ways to facilitate extracting experimental data on protein phosphorylation. Such information is of great value for biologists in studying cellular processes and diseases such as cancer and diabetes. Existing approaches like RLIMS-P are mainly rule based. The performance lays much reliance on the completeness of rules. We propose an SVM-based system known as MinePhos which outperforms RLIMS-P in both precision and recall of information extraction when tested on a set of articles randomly chosen from PubMed.

Higher-powered relay nodes have been proposed as cluster heads in hierarchical sensor networks to increase the network connectivity, coverage and lifetime. Determining an appropriate placement scheme of the relay nodes that ensures adequate coverage and connectivity, while using a minimum number of relay nodes, is an important design problem and a significant amount of work has been done in this area in recent years. However, most of the existing placement strategies typically assume only stationary nodes, where data of each relay nodes (received from the underlying sensor nodes in its cluster) are routed to the base station(s), using either single-hop or multi-hop routing schemes. Recently, the use of mobile data collectors (MDC) has been shown to improve the network performance in a variety of sensor network applications. In this paper, we consider a hierarchical relay node based network, where a mobile data collector moves along a fixed trajectory, collects data from each relay node and delivers them to the base station. Such a model reduces the energy dissipation of the relay nodes by relieving them of the burden of transmitting data over longer distances, thereby increasing the overall lifetime of the network. The issue is to find the minimum number of relay nodes, along with their locations such that all network requirements are satisfied. We present an integrated integer linear program (ILP) formulation that takes into consideration the sensor data rates, the relay nodes buffer size and the speed of the MDC, and determines an optimal relay node placement scheme, which ensures that there is no data loss due to relay node buffer overflow and the energy dissipation does not exceed a specified level.