Homomorphic encryption (HE) is one of the more sophisticated methods of homomorphic cryptography (HC). HC efficiently contacts the interacting parties in open IoT and light-weighted network environments. This approach is capable of analyzing encrypted data without decryption. The operations use private and public keys. Then, during the assessment or evaluation, users may access the original data. Before conducting tests or evaluations, the customer must first encrypt the data and then decrypt it. Since consumers use several main cycles for the whole operation, which creates noise and computation overheads, the growth rate of computation overheads has increased. The growing ratio of noise to computation rate can interrupt the whole system, resulting in machine instability, protection, and privacy concerns. To resolve the security and privacy issues, the proposed schemes used different hardness assumptions, such as over-integer, learning with error, ideal lattices, bootstrapping, etc. In this article, we presents a comprehensive review of HE and its many varieties. The numerous possible applications of HE are covered at a high level in order to highlight the extent to which HE is used in the IoT and other lighted-weighted intelligent industry environments in a variety of various domains.

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. Blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing Big Data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes.

Current research in human activity recognition primarily emphasizes enhancing accuracy, with limited exploration into computational efficiency and hardware compatibility. Recently, Mamba has sparked substantial interest within the realm of deep learning. Mamba is a hardware-aware algorithm enabling very efficient training and inference. Researchers are applying Mamba to various tasks, demonstrating significant promise in both language and vision tasks. It is worthwhile to investigate the use of Mamba for efficient human activity recognition. In this paper, we proposed a hybrid neural network that integrates CNN and visual Mamba, called ActivityMamba. The SE-Mamba block in ActivityMamba utilizes both CNN’s local and Mamba’s global context modeling while keeping computation and memory efficiency. We evaluated the ActivityMamba on five public benchmark datasets collected by using three different sensing techniques. ActivityMamba achieved higher performance than vision transformers, vision Mamba, and CNNs with fewer FLOPs and parameters. It sets a new SOTA on all five datasets, which are 91.78% OA and 89.13% F1 on the USC-HAD dataset, 99.19% OA and 98.64% F1 on the UT-HAR dataset, 99.82% OA and F1 on the DIAT dataset, 98.59% OA and 98.65% F1 on the UCI-HAR dataset, and 95.41% OA and 93.14% F1 on the UniMib dataset. Our work is the first to investigate the CNN-Mamba hybrid network for efficient human activity recognition.

Transformer architectures are popularized in both vision and natural language processing tasks, and they have achieved new performance benchmarks because of their long-term dependencies modeling, efficient parallel processing, and increased model capacity. While transformers offer powerful capabilities, their demanding computational requirements clash with the real-time and energy-efficient needs of edge-oriented human activity recognition. It is necessary to compress the transformer to reduce its memory consumption and accelerate the inference. In this paper, we investigated the binarization of a transformer-DeepViT for efficient human activity recognition. For feeding sensor signals into DeepViT, we first processed sensor signals to spectrograms by using wavelet transform. Then we applied three methods to binarize DeepViT and evaluated it on three public benchmark datasets for sensor-based human activity recognition. Compared to the full-precision DeepViT, the fully binarized one (Bi-DeepViT) reduced about 96.7% model size and 99% BOPs (Bit Operations) with only a little accuracy compromised. Furthermore, we explored the effects of binarizing various components and latent binarization of DeepViT to understand their impact on the model. We also validated the performance of Bi-DeepViTs on two wireless sensing datasets. The result shows that a certain partial binarization can improve the performance of DeepViT. Our work is the first to apply a binarized transformer in HAR.

The integration of 5G/6G networks with intelligent healthcare systems has enabled early disease detection through patient data monitoring. However, the Internet of Medical Things (IoMT) and remote healthcare services introduce significant privacy and security risks. In this paper, we propose LiteCrypt, which addresses these challenges by introducing an optimized Homomorphic Convolutional Neural Networks (HCNN) structure for secure inference and a lightweight Threshold Signature Scheme (TSS) based dual-authorization mechanism. To enhance the practicality of Homomorphic Encryption (HE)-based secure inference in telemedicine applications, LiteCrypt presents an optimized HCNN framework that ensures efficient and adaptable operations across multiple datasets. A high-performance GPU-accelerated HE engine is developed to address the computational demands of HE operations, enabling real-time processing of encrypted patient data. Besides, LiteCrypt introduces a novel TSS-based dual-authorization protocol, requiring consent from both the patient and the hospital to access patient data, thereby mitigating unauthorized access risks. The system adapts to a flexible 2-out-of-3 authorization scheme for emergencies, ensuring timely data retrieval while maintaining security. To overcome the initial challenge of prolonged computation time due to compute-intensive operations, In LiteCrypt, we utilized the lightweight TSS protocol, based on Oblivious Transfer (OT), which is designed for resource-constrained IoMT devices, reducing computation time from 11.9 to 0.11 seconds. Empirical validation demonstrates LiteCrypt’s superior performance, achieving a 233-fold increase in processing speed, a $96 \%$ reduction in encrypted message size, and a 28-fold speed increase using GPUs.

With the advancement of the Internet of Medical Things (IoMT) and cryptographic technologies, remote healthcare services have become more widespread, presenting new challenges for patient privacy and data security. Conventional security mechanisms, such as centralized authentication and key distribution systems, are susceptible to single points of failure and significant management burdens, potentially leading to compromised authentication centers and internal security threats. In response, this study presents a threshold signature algorithm, it uses Distributed Key Generation (DKG) that distributes private keys without the need for a trusted key distributor, requiring the cooperative signature of at least two nodes for authentication. This approach not only circumvents the risk of single points of failure but also enhances the system’s robustness and efficiency. The experimental results validate its prospective utility in safeguarding remote healthcare data.

With recent progress in Internet of Things technology, it is becoming more and more commonplace to use unmanned aerial vehicles (UAVs) for inconsiderable purposes. On the other hand, traditional all of these UAV networks adopt a susceptible open wireless communication, rendering these systems vulnerable to attacks like eavesdropping, tampering, interrupting, and forging. The most effective way to address these security challenges is through signcryption. However, current signcryption methods are computationally and bandwidth-intensive, making them unsuitable for UAVs with limited resources and ground stations (GS) handling a high volume of messages. To address these challenges, we propose a solution employing an identity-based online/offline signcryption scheme to secure communication from a UAV to GS, known as IOOSC-U2G. This scheme leverages elliptic curve cryptography without the need for time-intensive operations like bilinear pairing. During the online phase, the absence of point multiplication operations, already executed in the offline phase, significantly alleviates computational burdens. This optimization significantly reduces computational overhead throughout the entire signcryption process of messages. Moreover, the IOOSC-U2G scheme ensures the privacy of UAV identities during communication with GS. Additionally, the proposed scheme empowers the GS to verify multiple inputs at once through batch verification method. We demonstrate that within the random oracle model, the IOOSC-U2G scheme guarantees security, specifically confidentiality and unforgeability, relying on the computational hardness assumptions of the elliptic curve inverse Computational Diffie-Hellman problem and the elliptic Discrete Logarithm problem, respectively. Moreover, our scheme outperforms current methods, particularly in computational and communication efficiency.

Wireless sensing and communication evolved separately in the past. However, Integrated Sensing and Communication (ISAC) unlocks a new era of mobile network capabilities, with WiFi emerging as a prime candidate. By leveraging existing WiFi infrastructure and frequencies, ISAC enables powerful services like accurate localization and human activity recognition (HAR). WiFi-based HAR is a prime example powered by the magic of ISAC. WiFi Channel State Information (CSI) is susceptible to human movement disturbances; the alterations in CSI mirror the dynamic attributes of human activities. Given the intricate relationship between human activities and CSI, numerous deep learning models have been introduced to enhance HAR accuracy. Recently, transformer-based models have achieved excellent performance in various tasks, including speech recognition, natural language processing, and image classification. This has spurred research into incorporating transformer-based models into WiFi sensing applications. However, their application in WiFi-based HAR remains nascent. Vision transformer is well-suited for analyzing WiFi CSI signals in the form of spectra, such as the Doppler frequency spectrum frequently utilized in related studies, owing to its data structure mimicking that of images. In this study, we explored five widely used Vision Transformer architectures (vanilla ViT, SimpleViT, DeepViT, SwinTransformer, and CaiT) for WiFi CSI-based HAR using two publicly available datasets, UT-HAR and NTU-Fi HAR. Our work aims to assess and compare the performance of diverse ViT architectures for WiFi CSI-based HAR and provide guidelines for WiFi-based HAR modeling and ViT selection, considering accuracy, model size, and computational efficiency.

Federated learning (FL) offers distributed machine learning on edge devices. However, the FL model raises privacy concerns. Various techniques, such as homomorphic encryption (HE), differential privacy, and multiparty cooperation, are used to address the privacy issues of the FL model. Among them, HE ensures greater security and privacy since end-to-end encryption maintains data privacy throughout the computation process. Compared with other privacy-preserving techniques, HE does not require the establishment of a trusted environment or protocol among multiple parties and does not involve any artificial noise that can impair system performance. Unfortunately, it suffers from efficiency overhead when applied to privacy-preserving FL (PPFL). Some existing surveys on PPFL discuss the generic construction and organization of PPFL from the perspective of practical HE deployment in PPFL. However, none of them covers the efficiency optimization of HE when applied to PPFL. This article conducts a comprehensive review of the efficiency optimization of HE when applied to PPFL. First, we review general optimization strategies and discuss their limitations when applied directly to HE-based PPFL. Second, an overview of algorithmic, hardware, and hybrid optimizations is provided, along with a discussion of their adaptation. Additionally, we provide a detailed taxonomy of optimizations. Finally, we suggest future HE-based PPFL research directions.

Human activity recognition (HAR) has become a research hotspot because of its wide range of application prospects. It has higher requirements for real-time and powerefficient processing. However, a large amount of data transfer between sensors and servers, and computation-intensive recognition models hinder the implementation of real-time HAR systems. Recently, edge computing has been proposed to address this challenge by moving computational and data storage resources to the sensors, rather than depending on a centralized server/cloud. In this paper, we investigated binary neural networks for edge intelligence-enabled HAR using radar point cloud. Point cloud can provide 3-dimensional spatial information, which is helpful to improve recognition accuracy. Time-series point cloud also brings challenges, such as larger data volume, 4-dimensional data processing, and more intensive computation. To tackle these challenges, we adopt the 2-dimensional histograms for point cloud multi-view processing and propose the EdgeActNet, a binary neural network for point cloud-based human activity classification on edge devices. In the evaluation, the EdgeActNet achieved the best results with average accuracies of 97.63% on the MMActivity dataset and 95.03% on the point cloud samples of the DGUHA dataset respectively; and saved 16.9× memory consumption and 11.5× inference time compared to its full-precision version. Our work also is the first to apply 2D histogram-based multi-view representation and BNNs for timeseries point cloud classification.

The rapid development of AI applications powered by deep learning in edge devices boosts the opportunity for real-time health monitoring. To address the potential privacy concern in the inference phase, homomorphic encryption (HE) is an alternative solution that encrypts inference data without exposing raw data and has several distinct advantages, (i.e., single-round communication, lightweight bandwidth consumption, and non-interactive computation). However, the computational overhead on the current HE-based privacy-preserving inference necessitates a substantial amount of time, which is not feasible for some real-time applications on edge devices. To address this issue, we propose CNN-guardian, a unified and compact neural network structure for real-time inference in HE-based inference on edge GPU. CNN-guardian designs a HE-friendly neural network and GPU engine that optimizes HE operations to accelerate the inference in the HE domain.

CT vertebral segmentation plays an essential role in various clinical applications, such as computer‐assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch‐based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)‐module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high‐level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high‐level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI‐Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F‐score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model’s performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application.

Wearable devices equipped with a variety of sensors facilitate the measurement of physiological and behavioral characteristics. Activity-based person identification is considered an emerging and fast-evolving technology in security and access control fields. Wearables, such as smartphones, Apple Watch, and Google glass can continuously sense and collect activity-related information of users, and activity patterns can be extracted for differentiating different people. Although various human activities have been widely studied, few of them (gaits and keystrokes) have been used for person identification. In this article, we performed person identification using two public benchmark data sets (UCI-HAR and WISDM2019), which are collected from several different activities using multimodal sensors (accelerometer and gyroscope) embedded in wearable devices (smartphone and smartwatch). We implemented eight classifiers, including an multivariate squeeze-and-excitation network (MSENet), time-series transformer (TST), temporal convolutional network (TCN), CNN-LSTM, ConvLSTM, XGBoost, decision tree, and$k$-nearest neighbor. The proposed MSENet can model the relationship between different sensor data. It achieved the best person identification accuracies under different activities of 91.31% and 97.79%, respectively, for the public data sets of UCI-HAR and WISDM2019. We also investigated the effects of sensor modality, human activity, feature fusion, and window size for sensor signal segmentation. Compared to the related work, our approach has achieved the state of the art.

Radar-based human activity recognition is attracting a wide range of interest from both industry and academia because of its through-wall ability, privacy-preserving capability, and device-free detection. Currently, most radar-based systems consider signal analysis and feature extraction in the frequency domain or the temporal domain independently without fusing them together. In this article, in order to model both frequency properties and temporal profiles of human activity, we proposed a spectro-temporal network (STnet) that integrates a temporal convolutional network (TCN) and a convolutional neural network (CNN). It can extract temporal patterns and micro-Doppler features from radar signals for human activity recognition. In the experiments, two radar sensors and one base station were used to build a low-power wireless radar sensor network. Fifteen activities were investigated in a real kitchen scenario by using this radar sensor network. Frequency spectrograms were obtained after signal processing using a short-time Fourier transform (STFT). They were further segmented using a short sliding window (2.5 s), which enables a very small latency. The proposed STnet achieved 99.64% overall accuracy (OA) in testing, which is superior to the other three networks that we implemented in this work. Our work also can be used as a generic solution to other sensor-based (wearable sensors, WiFi channel state information (CSI), etc.) activity recognition.

A wide diversity of sensors has been applied in human activity recognition. These sensors generate enormous amounts of data during human activity monitoring. The long-distance data traveling between sensors and servers increases the costs of bandwidth and latency. However, human activity recognition has a high demand for real-time processing. Recently, edge computing is surging to solve this problem by moving computation and data storage closer to the sensor devices, rather than relying on a central server/cloud. Edge servers are usually designed for low power, low cost, and low computation. They do not support computation-intensive deep learning algorithms or will result in high latency. Fortunately, the development of binarized neural networks enables edge intelligence which supports AI running at the network edge for real-time applications. In this paper, we implement a binarized neural network (BinaryDilatedDenseNet) to enable low-latency and low-memory human activity recognition at the network edge. We applied the BinaryDilatedDenseNet on three sensor-based human activity recognition datasets and evaluated it with four metrics. In comparison, the BinaryDilatedDenseNet outperforms the related work and other three binarized neural networks in accuracy and saves 10 memory and 4.5--8 inference time compared to the FPDilatedDenseNet(the full-precision version of the BinaryDilatedDenseNet).

This paper integrates Software-Defined Networking (SDN) and Information -Centric Networking (ICN) framework to enable low latency-based stateful routing and caching management by leveraging a novel forwarding and caching strategy. The framework is implemented in a clean- slate environment that does not rely on the TCP/IP principle. It utilizes Pending Interest Tables (PIT) instead of Forwarding Information Base (FIB) to perform data dissemination among peers in the proposed IC-SDN framework. As a result, all data exchanged and cached in the system are organized in chunks with the same interest resulting in reduced packet overhead costs. Additionally, we propose an efficient caching strategy that leverages in- network caching and naming of contents through an IC-SDN controller to support off- path caching. The testbed evaluation shows that the proposed IC-SDN implementation achieves an increased throughput and reduced latency compared to the traditional information-centric environment, especially in the high load scenarios.

Current Transport Layer Security (TLS) Public-Key Infrastructure (PKI) is a vast and complex system; it consists of processes, policies, and entities that are responsible for a secure certificate management process. Among them, Certificate Authority (CA) is the central and most trusted entity. However, recent compromises of CA result in the desire for some other secure and transparent alternative approaches. To distribute the trust and mitigate the threats and security issues of current PKI, publicly verifiable log-based approaches have been proposed. However, still, these schemes have vulnerabilities and inefficiency problems due to lack of specifying proper monitoring, data structure, and extra latency. We propose Accountable and Transparent TLS Certificate Management: an alternate Public-Key Infrastructure (PKI) with verifiable trusted parties (ATCM) that makes certificate management phases; certificate issuance, registration, revocation, and validation publicly verifiable. It also guarantees strong security by preventing man-in-middle-attack (MitM) when at least one entity is trusted out of all entities taking part in the protocol signing and verification. Accountable and Transparent TLS Certificate Management: an alternate Public-Key Infrastructure (PKI) with verifiable trusted parties (ATCM) can handle CA hierarchy and introduces an improved revocation system and revocation policy. We have compared our performance results with state-of-the-art log-based protocols. The performance results and evaluations show that it is feasible for practical use. Moreover, we have performed formal verification of our proposed protocol to verify its core security properties using Tamarin Prover.

The primary objective of this research is to propose and investigate a novel ant colony optimization-based classification rule discovery algorithm and its variants. The main feature of this algorithm is a new heuristic function based on the correlation between attributes of a dataset. Several aspects and parameters of the proposed algorithm are investigated by experimentation on a number of benchmark datasets. We study the performance of our proposed approach and compare it with several state-of-the art commonly used classification algorithms. Experimental results indicate that the proposed approach builds more accurate models than the compared algorithms. The high accuracy supplemented by the comprehensibility of the discovered rule sets is the main advantage of this method.