Signcryption Technique Research Articles

Enhancing security and privacy in smart agriculture: A novel homomorphic signcryption system

Technology driven Smart Agriculture is transforming modern day farming practices through digital tools and real-time data analysis. It monitors plant health in both soil and soilless cultivation, promoting environmentally responsible practices by utilizing integrated sensors and precision methods. Despite the significant potential benefits, challenges persist in ensuring data security in the smart agriculture frameworks. The existing research has explored various mechanisms to cater these issues, yet these solutions susceptible to known attacks i.e., Confidentiality, Authentication, Privacy preservation. Notably, the available schemes in open literature face performance deficiencies due to an overreliance on Rivest-Shamir-Adleman (RSA), bilinear pairing, and elliptic curve (EC). To comprehensively address these challenges, this study presents a novel approach via employing homomorphic Signcryption based on Hyper-Elliptic Curves (HEC). Our HEC based Signcryption technique enhances security with lower computational cost and communication overhead as compared to the existing techniques. Furthermore, our solution provides the best performance in terms of efficiency, addressing the privacy preservation issues with conformance to the (Confidentiality, Integrity, and Availability) CIA triad. For proof of concept and correctness, we have validated the results in Scyther tool, with the assurance that proposed scheme is correct, verified, and resistant against known set of attacks. The proposed scheme achieves a remarkable reduction in both computational and communication costs around 95.08 % and 89.28 % respectively, enhancing practicality and suitability for real-world implementation. Thus, making it a suitable candidate for employing in smart agriculture environments, encompassing both soil and soilless cultivation scenarios. This research contributes a robust, secure, cost effective, and efficient solution to the challenges posed by the evolving landscape of agriculture, paving the way for enhanced data protection and system resilience in smart agriculture systems.

Advanced set containment deep learned Rabin certificateless signcryption for secured transmission with big data in cloud

SummaryCloud computing is distributed type of technology to offers distant services by the internet to manage, access, and store data on their server. The cloud server manages and distributes large amounts of data. During large amounts of complex structured and unstructured data transactions, in order to protect the data and cloud resources from harmful activities by using the main task of the security system. Therefore, a novel intrusion detection system is required in cloud networks to detect malicious activity and improve secure data transmission. In this paper, a novel technique called advanced set containment deep learned Rabin certificateless signcryption (ADRES) technique is employed to secure data transmission for achieving better confidentiality, and integrity, in cloud computing. The ADRES technique includes three major phases namely Registration and key generation, signcryption, and unsigncryption. At the first phase, the user registers details to the cloud server using secure transmission in big data analytics. The cloud server generates the private and public keys for each registered user. In the second phase, the signcryption process is determined by cloud user that encrypts and generates the signature by using the Rabin cryptosystem. Then encrypted data is stored in a cloud server with minimum space complexity. Finally, the unsigncryption is performed by using signature verification and decryption. First, the signature verification is performed by applying an Advanced set containment Deep belief neural network for identifying the authorized or unauthorized user (i.e., attacker). The authorized user decrypts data using the Rabin cryptosystem to achieve the original data. Thus, secure data transmission is obtained through improved data confidentiality and integrity. Simulation of the proposed ADRES technique is performed with respect to data confidentiality level, data integrity rate, and computation time, space complexity with several numbers of data. Experimental results reported ADRES obtains the improved data transmission security with better confidentiality rate by 7%, integrity by 7%, and lesser time 16% and space complexity by 18% than the conventional methods.

Optimal Deep Learning Model Enabled Secure UAV Classification for營ndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

Enhanced security using proxy signcryption technique for wireless mesh networks

Enhanced security using proxy signcryption technique for wireless mesh networks

A provably secure, shortest, and efficient identity based signcryption technique in the standard model

AbstractSingcryption is a very important concept to bind confidentiality and authenticity. This is a logical step to reduce both computation and communication costs. The proposed an identity based signcryption that removes the overhead of certificates. The proposed design is provably secure in the standard model (a widely accepted model). Furthermore, the IBSC design uses modified bilinear Diffie‐Hellman inversion and modified strong bilinear Diffie‐Hellman assumption as building blocks of the security. The proposed design is secure, efficient, and shortest to the best of our knowledge. In this design, a user does pairing free computation, and he sends only a triplet on the public channel, that makes it efficient in terms of computation and communication costs.

Intelligent compression then encryption scheme for resource constrained sustainable and smart healthcare environment

With the latest and emerging developments of artificial technologies, ubiquitous computing techniques become necessary in day to day lives, particularly in healthcare. Smart healthcare systems involve electronic health records (EHR) sharing system that considerably shares the medical data and delivers proper aid to the patients and research communities. At the same time, the smart healthcare system is a resource constraint environment where large amount of healthcare data needs to be stored on the cloud server. Therefore, data compression can significantly reduce a large amount of data transmission and storage process, thereby achieving effective memory and resource utilization. Compression then encryption (CTE) is an effective way to achieve effective resource utilization and security in the smart healthcare environment. Encryption transforms the EHR data to an illegible format offering considerable authentication of the digital form of medicinal data. With this motivation, this paper designs a novel compression then encryption model for secure healthcare data storage and access management (CTE-SHDSA) technique in cloud server. The proposed model involves two main stages namely compression and encryption. Initially, neighboring indexing sequence (NIS) is employed for the data compression process. The NIS-BWT technique makes use of the correlation among the neighboring bits for reducing the amount of redundant data transmission. Besides, an enhanced artificial butterfly optimization with signcryption (EABO-SC) technique is applied to proficient encrypt the compressed data. The EABO algorithm is applied to optimally choose the encryption keys of the SC technique. The extensive set of simulations was performed on benchmark medical datasets and the outcomes are inspected under several evaluation parameters. The experimental values highlighted the superior performance of the presented method on the recent state of art methods.

Anonymous and Linkable Location-Based Services

In the location-based services (LBS) scheme, the vehicle user sends his query to the LBS server and gets the data corresponding to the requested query from it. Therefore, vehicle user’s query privacy and LBS server’s data privacy are serious issues in LBS. Several LBS schemes, like <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity, private-information retrieval, and homomorphic encryption-based, have been proposed to solve these issues. These schemes either provide vehicle user’s query privacy or LBS server’s data privacy. Some of the oblivious transfer (OT) protocol-based LBS schemes preserve vehicle users’ query privacy and LBS server’s data privacy. However, the OT-based LBS schemes do not preserve vehicle user’s identity privacy. Moreover, the efficiency (communication and computation costs) of the OT protocol-based LBS schemes depends on the LBS server’s database size, which is very high. In this work, we propose an anonymous linkable location-based services (AL2BS) scheme using linkable ring signcryption technique. The scheme provides vehicle user’s query privacy and LBS server’s data privacy without revealing the vehicle user’s identity. The efficiency of the AL2BS scheme is independent of the size of the LBS server’s database. The AL2BS scheme requires only one pairing operation on both ends and a few point multiplications. The theoretical and experimental results show that the performance of the AL2BS scheme in terms of communication and computation costs is better than the existing LBS schemes.

Cloud Storage Data Verification Using Signcryption Scheme

Cloud computing brings convenience to the users by providing computational resources and services. However, it comes with security challenges, such as unreliable cloud service providers that could threaten users’ data integrity. Therefore, we need a data verification protocol to ensure users’ data remains intact in the cloud storage. The data verification protocol has three important properties: public verifiability, privacy preservation, and blockless verification. Unfortunately, various existing signcryption schemes do not fully provide those important properties. As a result, we propose an improved version of a signcryption technique based on the short signature ZSS that can fulfill the aforementioned data verification important properties. Our computational cost and time complexity assessment demonstrates that our suggested scheme can offer more characteristics at the same computational cost as another ZSS signcryption scheme.

OQR-SC: An Optimal QoS Aware Routing Technique for Smart Cities Using IoT Enabled Wireless Sensor Networks

Internet of things (IoT) makes a machines optimization in everyday which processing the data by very intelligently and make communication more effectively and efficiently. In smart city environments, the data collection and communication play an important role in defining quality. Since the research period, it has been recommending a new data acquisition and data communication software framework for IoT smart applications. For further improvements, we recommend an optimal QoS aware routing technique for smart cities using IoT enabled wireless sensor networks (OQR-SC). In data gathering phase, the proposed system introduce chaotic bird swarm optimization (CBSO) algorithm for IoT sensor cluster formation; the improved differential search (IDS) algorithm used to estimate the faith degree of each sensor node, the highest trust node act as cluster head (CH). In data transferring phase, first illustrates lightweight signcryption technique for data encryption between two IoT sensors. Then, we use optimal decision making (ODM) algorithm to compute the optimal path between source–destination in IoT platform. Finally, the proposed OQR-SC technique is implemented using network simulation (NS2) tool and analyzes the performance of proposed technique with existing state-of-art techniques. The result summarizes that average energy consumption of proposed OQR-SC technique is 12.39% lower than the existing techniques; the average network lifetime of proposed OQR-SC technique is 15.96% higher than the existing techniques; the average delay of proposed OQR-SC technique is 21.08% lower than the existing techniques; and the average throughput of proposed OQR-SC technique is 17.89% higher than the existing techniques.

(Retracted) Blockchain enabled secure image transmission and diagnosis scheme in medical cyber-physical systems

Medical cyber-physical systems (MCPSs) are life critical, context aware, networked systems of medical devices that are increasingly used in hospitals to achieve seamless high-quality healthcare. The design of the MCPS for the healthcare sector necessitates significant attention to achieving security. As the medical images need to be communicated regularly for timely and accurate diagnosis, medical images need to be secured by encryption and blockchain technologies. In this aspect, we present a blockchain enabled secure image transmission and diagnosis (BESITD) for the MCPS environment. The BESITD technique encompasses an image acquisition process that enables the wearable devices to capture the medical images. Then, the presented model executes an intrusion detection system using recurrent neural network to determine the presence of intruders in the MCPS. In addition, the block-wise encryption process takes place in which the medical image is partitioned into n blocks, each of which is individually encrypted using the signcryption technique. Moreover, a consortium blockchain technology is used to store the encrypted image along with the hash value of the original medical image to accomplish integrity and traceability. At the cloud server side, the disease diagnosis process takes place in different stages, namely, multilevel thresholding-based segmentation, MobileNet-based feature extraction, and optimal kernel extreme learning machine (OKELM)-based classification. Furthermore, a multiobjective political optimizer is designed for effective selection of threshold values and KELM parameters. A wide range of simulations was performed on two benchmark medical image datasets, and the experimentation results highlighted the promising performance of the BESITD technique over the recent techniques with the maximum accuracy of 0.9816.

An Efficient Online/Offline Signcryption Scheme for Internet of Things in Smart Home

The delivery of unified intelligent services is accomplished through a networked environment comprised of a wide array of electronic devices. Through the use of Internet of Things (IoT) technology, smart homes collect data from their surroundings and use it to improve their tenants’ lives. Remote control, real-time monitoring, and a fire alarm are all characteristics of smart home security. Since smart homes hold personally identifying information about their residents, security is critical to ensure their reliability and prevent data breaches. In this paper, a certificateless online/offline signcryption (COOS) technique for IoT-enabled smart homes is proposed. The proposed solution takes advantage of a resource-constrained smart home device–friendly algorithm known as the Hyperelliptic Curve Cryptosystem (HCC). The suggested approach satisfies the security requirements of unforgeability, confidentiality, resistance to replay attacks, and non-repudiation. The complexity analysis in terms of communicational and computational costs demonstrates the efficiency of the proposed scheme. Finally, we validate the security against Man-In-The-Middle-Attack (MITM) and anti-reply attacks using Automated Validation of Internet Security Protocols and Applications (AVISPA). The data imply that the recommended course of action is safe.

A Lightweight Anonymous Authentication Protocol for Resource-Constrained Devices in Internet of Things

With the rapid development of Internet of Things (IoT) in recent years, the security of IoT becomes more and more prominent. To protect data privacy and device availability, identity authentication technology has been applied to the IoT environment. However, although scholars have designed a variety of authentication protocols for the IoT environment, the resource costs of these protocols are still expensive for resource-constrained devices. In particular, when the frequent upgrade of computer hardware has led to a gradual increase in the key length of the cryptosystem, IoT devices are hard to keep up with the upgrade speed because of the balance between cost and tasks. Although the storage space has increased in recent years, the computational and communication capabilities are still limited by some physical factors, such as energy, power, and communication link bandwidth. To improve the situation, this article proposes an anonymous authentication protocol suitable for IoT devices with current hardware performance, which is based on elliptic curve and signcryption techniques. Compared to other protocols, our protocol further reduces the communication and computing cost of the client devices while ensuring security requirements. Thus, we formally prove its theoretical security under the random oracle model and use simulation experiments to verify that the protocol can effectively reduce the resource requirements.

A Comprehensive Survey on Signcryption Security Mechanisms in Wireless Body Area Networks.

WBANs (Wireless Body Area Networks) are frequently depicted as a paradigm shift in healthcare from traditional to modern E-Healthcare. The vitals of the patient signs by the sensors are highly sensitive, secret, and vulnerable to numerous adversarial attacks. Since WBANs is a real-world application of the healthcare system, it’s vital to ensure that the data acquired by the WBANs sensors is secure and not accessible to unauthorized parties or security hazards. As a result, effective signcryption security solutions are required for the WBANs’ success and widespread use. Over the last two decades, researchers have proposed a slew of signcryption security solutions to achieve this goal. The lack of a clear and unified study in terms of signcryption solutions can offer a bird’s eye view of WBANs. Based on the most recent signcryption papers, we analyzed WBAN’s communication architecture, security requirements, and the primary problems in WBANs to meet the aforementioned objectives. This survey also includes the most up to date signcryption security techniques in WBANs environments. By identifying and comparing all available signcryption techniques in the WBANs sector, the study will aid the academic community in understanding security problems and causes. The goal of this survey is to provide a comparative review of the existing signcryption security solutions and to analyze the previously indicated solution given for WBANs. A multi-criteria decision-making approach is used for a comparative examination of the existing signcryption solutions. Furthermore, the survey also highlights some of the public research issues that researchers must face to develop the security features of WBANs.

Novel Image Encryption and Compression Scheme for IoT Environment

Latest advancements made in the processing abilities of smart devices have resulted in the designing of Intelligent Internet of Things (IoT) environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless Multimedia Surveillance Networks (WMSNs) form a vital part in IoT-assisted environment since it contains visual sensors that examine the surroundings from a number of overlapping views by capturing the images incessantly. Since IoT devices generate a massive quantity of digital media, it is therefore required to save the media, especially images, in a secure way. In order to achieve security, encryption techniques as well as compression techniques are employed to reduce the amount of digital data, being communicated over the network. Encryption Then Compression (ETC) techniques pave a way for secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presents a new ETC technique to accomplish image security in IoT environment. The proposed model involves three major processes namely, IoT-based image acquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NMWOA) abbreviated as ST-NMWOA. The optimal key generation of signcryption technique takes place with the help of NMWOA. Besides, the presented model also uses Discrete Fourier Transform (DFT) and Matrix Minimization (MM) algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed model. The obtained values infer that the presented model is superior in terms of both compression efficiency and data secrecy in resource-limited IoT environment.

Intelligent Optimization-Based Clustering with Encryption Technique for Internet of Drones Environment

The recent technological developments have revolutionized the functioning of Wireless Sensor Network (WSN)-based industries with the development of Internet of Things (IoT). Internet of Drones (IoD) is a division under IoT and is utilized for communication amongst drones. While drones are naturally mobile, it undergoes frequent topological changes. Such alterations in the topology cause route election, stability, and scalability problems in IoD. Encryption is considered as an effective method to transmit the images in IoD environment. The current study introduces an Atom Search Optimization based Clustering with Encryption Technique for Secure Internet of Drones (ASOCE-SIoD) environment. The key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by drones. The presented ASOCE-SIoD technique follows ASO-based Cluster Head (CH) and cluster construction technique. In addition, signcryption technique is also applied to effectually encrypt the images captured by drones in IoD environment. This process enables the secure transmission of images to the ground station. In order to validate the efficiency of the proposed ASOCE-SIoD technique, several experimental analyses were conducted and the outcomes were inspected under different aspects. The comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.

Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

Internet of Things (IoT) and blockchain receive significant interest owing to their applicability in different application areas such as healthcare, finance, transportation, etc. Medical image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public networks. This paper presents a new wind driven optimization algorithm based medical image encryption (WDOA-MIE) technique for blockchain enabled IoT environments. The WDOA-MIE model involves three major processes namely data collection, image encryption, optimal key generation, and data transmission. Initially, the medical images were captured from the patient using IoT devices. Then, the captured images are encrypted using signcryption technique. In addition, for improving the performance of the signcryption technique, the optimal key generation procedure was applied by WDOA algorithm. The goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio (PSNR). Upon successful encryption of images, the IoT devices transmit to the closest server for storing it in the blockchain securely. The performance of the presented method was analyzed utilizing the benchmark medical image dataset. The security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

Lamport Certificateless Signcryption Deep Neural Networks for Data Aggregation Security in WSN

Confidentiality and data integrity are essential paradigms in data aggregation owing to the various cyberattacks in wireless sensor networks (WSNs). This study proposes a novel technique named Lamport certificateless signcryption-based shift-invariant connectionist artificial deep neural networks (LCS-SICADNN) by using artificial deep neural networks to develop the data aggregation security model. This model utilises the input layer with several sensor nodes, four hidden layers to overcome different attacks (data injection, compromised node, Sybil and black hole attacks) and the output layer to analyse the given input. The Lamport one-time certificateless signcryption technique involving three different processes (key generation, signcryption and unsigncryption) is adopted to achieve secure data transmission between the sender and receiver. Firstly, a one-way function is executed to generate the public and private keys for each sensor node in the WSN. Secondly, digital signature generation and encryption are both performed. The sender node, which handles the signature generation and data encryption, forwards the data to the aggregator node. Then, the receiver verifies the data by using the sender’s public key during data decryption. Thus, data aggregation security can be guaranteed. Finally, the authorised node aggregates the data with much higher data confidentiality and privacy. Performance analysis is conducted by simulating the proposed LCS-SICADNN and conventional models. Results of comparison indicate that the LCS-SICADNN can improve data aggregation security with higher throughput and lesser delay, packet drop and overhead compared with the conventional methods.

CGST: Provably Secure Lightweight Certificateless Group Signcryption Technique Based on Fractional Chaotic Maps

In recent years, there has been a lot of research interest in analyzing chaotic constructions and their associated cryptographic structures. Compared with the essential combination of encryption and signature, the signcryption scheme has a more realistic solution for achieving message confidentiality and authentication simultaneously. However, the security of a signcryption scheme is questionable when deployed in modern safety-critical systems, especially as billions of sensitive user information is transmitted over open communication channels. In order to address this problem, a lightweight, provably secure certificateless technique that uses Fractional Chaotic Maps (FCM) for group-oriented signcryption (CGST) is proposed. The main feature of the CGST-FCM technique is that any group signcrypter may encrypt data/information with the group manager (GM) and have it sent to the verifier seamlessly. This implies the legitimacy of the signcrypted information/data is verifiable using the public conditions of the group, but they cannot link it to the conforming signcrypter. In this scenario, valid signcrypted information/data cannot be produced by the GM or any signcrypter in that category alone. However, the GM is allowed to reveal the identity of the signcrypter when there is a legal conflict to restrict repudiation of the signature. Generally, the CGST-FCM technique is protected from the indistinguishably chosen ciphertext attack (IND-CCA). Additionally, the computationally difficult Diffie-Hellman (DH) problems have been used to build unlinkability, untraceability, unforgeability, and robustness of the projected CGST-FCM scheme. Finally, the security investigation of the presented CGST-FCM technique shows appreciable consistency and high efficiency when applied in real-time security applications.

Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies. Blockchain, data encryption, and deep learning (DL) models can be utilized to design efficient security solutions for IoT healthcare applications. In this aspect, this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning (BPPIE-ODL) technique for IoT healthcare applications. The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server. The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm (DFA) with signcryption technique to encrypt the medical images captured by the IoT devices. Besides, blockchain (BC) can be utilized as a distributed data saving approach for generating a ledger, which permits access to the users and prevents third party’s access to encrypted data. In addition, the classification process includes SqueezeNet based feature extraction, softmax classifier (SMC), and Nadam based hyperparameter optimizer. The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture. For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique, a comprehensive experimental analysis is carried out. The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.

An Effective Signcryption with Optimization Algorithm for IoT-enabled Secure Data Transmission

Internet of Things (IoT) allows several low resources and controlled devices to interconnect, calculate processes and make decisions in the communication network. In the heterogeneous environment for IoT devices, several challenging issues such as energy, storage, efficiency, and security. The design of encryption techniques enables the transmission of the data in the IoT environment in a secured way. The proper selection of optimal keys helps to boost the encryption performance. With this motivation, the study presents a signcryption with quantum chaotic krill herd algorithm for secured data transmission (SCQCKH-SDT) in IoT environment. The proposed SCQCKH-SDT technique aims to effectively encrypts the data by the use of optimal keys generated by the CQKH algorithm. The proposed SCQCKH-SDT technique initially employs the signcryption technique for the encryption of data. In order to optimize the secrecy, the optimal key generation process is carried out using Chaotic Krill Herd (CQKH) algorithm. The CQKH algorithm incorporates the concept of quantum computing and chaotic theory into the traditional KH algorithm. The performance validation of the SCQCKH-SDT technique is performed using benchmark dataset. An extensive comparative analysis reported the superior performance of the SCQCKH-SDT technique over the recent approaches.