Edge computing improves the performance of Internet of Things (IoT) devices by moving cloud services closer to where these devices operate, thereby reducing delays and saving bandwidth. However, the IoT devices communicate wirelessly with edge servers, which creates a significant challenge in keeping the devices' locations private, especially when they use different methods. To address this, researchers have proposed various methods. However, these methods require a lot of computational power and storage, which makes them unsuitable for edge computing environments. To address these challenges, we propose a certificateless cryptography (CLC) and identity-based cryptography (IBC) based heterogeneous deniable authentication (CI-HDA) scheme. It enables an IoT device operating CLC to securely communicate with an edge server using IBC. The server verifies the devices authenticity without proving its participation to third parties, which ensures location privacy in edge computing environments. Additionally, our scheme supports batch verification, which enables the server to efficiently validate multiple authenticators simultaneously. The security of CI-HDA scheme is formally proven in the random oracle model, and performance evaluations demonstrate reduced computational and communication/storage overheads compared to existing methods. We also explored its application in military surveillance, which highlights its practicality in privacy-sensitive edge computing environments.

Edge computing brings cloud services closer to Internet of Things (IoT) devices by processing data at the edge of the network, reducing latency and bandwidth usage. However, preserving the location privacy of IoT devices is a major challenge due to the heterogeneous, open wireless, and dynamic nature of the edge computing environments. The authentication process in this context may potentially disclose the device’s location, as IoT devices and edge server often rely on different security mechanisms. To address this concern, researchers have proposed various schemes to preserve location privacy. However, these schemes often incur high computational and communication overhead, making them unsuitable for IoT devices, which have limited processing power and storage capacity. In response to this challenge, we present a heterogeneous deniable authentication scheme based on certificateless cryptography (CLC) and public-key infrastructure (PKI), abbreviated as CP-HDA. This scheme enables an IoT device in a CLC to securely transmit messages to an edge server in a PKI. Using the CP-HDA scheme, the edge server can verify the legitimacy of the IoT device but cannot provide evidence to third parties regarding the device’s involvement in communication, thereby preserving the location privacy of the IoT device. It also supports batch verification, which enables faster verification of multiple deniable authenticators. The security of the CP-HDA scheme is formally proven in the random oracle model based on the assumption that the elliptic curve computational Diffie-Hellman problem is hard. In comparison to alternative schemes, our scheme enhances performance by reducing both computational and communication costs.

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 integration of Unmanned Aerial Vehicles (UAVs) with the Internet of Things (IoT), also known as IoUAVs, facilitates real-time data transmission and coordinated operations in critical applications such as smart agriculture, disaster response, and infrastructure monitoring. The growing development of IoT has, however, made IoUAVs vulnerable to emerging cyberattacks that could disrupt these essential services. Deep learning can detect hidden attack patterns, but power and processing constraints make it challenging for resource-constrained IoUAVs. Edge computing offloads real-time analysis tasks, but optimizing workloads with unpredictable connectivity and high latency requirements for intrusion detection remains challenging. To address these challenges, this paper proposes a novel Edge-Based Intrusion Detection System (EIDS) that introduces two key innovations. We developed a metaheuristic task optimization technique for the IoUAV edge environment to efficiently manage computational loads and resources. Second, a Deep Transfer Learning (DTL) technique optimized for intrusion detection minimizes training time and computational overhead. Our novel EIDS-DTL technology synergistically incorporates these components for powerful intrusion detection. Our method optimizes feature extraction from IoUAV network traffic by purifying, filtering, and normalizing data. By fine-tuning pre-trained models, the system achieves high accuracy in identifying malicious activity while ensuring optimal performance in resource-constrained environments. Experimental results on two benchmark datasets demonstrate classification accuracies of 98.95% and 99.27%, outperforming existing approaches by up to 5% in accuracy while maintaining high precision, recall, and F1 scores. The proposed method enhances accuracy and efficiency, providing an effective solution for IoUAV security and edge optimization.

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.

The healthcare industry is one of the most vulnerable to cybercrime and privacy violations because health data is very sensitive and spread out in many places. Recent confidentiality trends and a rising number of infringements in different sectors make it crucial to implement new methods that protect data privacy while maintaining accuracy and sustainability. Moreover, the intermittent nature of remote clients with imbalanced datasets poses a significant obstacle for decentralized healthcare systems. Federated learning (FL) is a decentralized and privacy-protecting approach to deep learning and machine learning models. In this article, we implement a scalable FL framework for interactive smart healthcare systems with intermittent clients using chest X-ray images. Remote hospitals may have imbalanced datasets with intermittent clients communicating with the FL global server. The data augmentation method is used to balance datasets for local model training. In practice, some clients may leave the training process while others join due to technical or connectivity issues. The proposed method is tested with five to eighteen clients and different testing data sizes to evaluate performance in various situations. The experiments show that the proposed FL approach produces competitive results when dealing with two distinct problems, such as intermittent clients and imbalanced data. These findings would encourage medical institutions to collaborate and use rich private data to quickly develop a powerful patient diagnostic model.