Encryption Time Research Articles

Hidden Real Modulus RSA Cryptosystem

Cryptographic techniques in cyber security can be categorized into symmetric and asymmetric. Among asymmetric cryptographic techniques, the RSA algorithm is more popular and considered as secured. Since, RSA uses the common modulus in both encryption and decryption, this modulus is openly available for the public which makes it exposed for attack. Its security is based on the assumption of large integer factorization problem, but this could leave it open to different cryptanalysis attacks: low private exponent attack, Shor’s polynomial-time quantum algorithm, quantum inverse Fourier transform and phase estimation. To address these shortcomings, this paper proposes a public-key security algorithm called Hidden Real Modulus RSA (HRM-RSA) which hides real modulus by masking it. The public mask modulus which is a pseudo random masking number is derived from real modulus. Then, this derived public mask modulus is introduced in a public key component; as a result, a real modulus is kept hidden from the public unlike the case in RSA. Encryption is done using this public mask modulus and the decryption process is done using a private hidden real modulus. For performance analysis Net bean IDE 8.2 is used, and the proposed algorithm is compared with state-of-the-art algorithms: RSA, ESRKGS, and MRSA based on security strength, time complexity, key generation time, encryption speed, and decryption speed. The performance analysis shows that HRM-RSA is less complex but highly secured than existing algorithms. It improves key generation time of ESRKGS, and MRSA by 311%, 42%; encryption time of RSA, ESRKGS, MRSA by 0.7%, 139%, 735%; decryption time of RSA, ESRKGS, MRSA by 3%, 138%, 799%, respectively.

Blockchain- Based Secure and Efficient Scheme for Medical Data

Internet of Things (IoT) fog nodes are distributed near end-user devices to mitigate the impacts of low delay, position awareness, and spatial spread, which aren't permitted by numerous IoT apps. Fog computing (FC) also speeds up reaction times by decreasing the quantity of data sent to the cloud. Despite these advantages, FC still has a lot of work to do to fulfill security and privacy standards. The constraints of the FC resources are the cause of these difficulties. In reality, FC could raise fresh concerns about privacy and security. Although the Fog security and privacy problems have been covered in several articles recently, most of these studies just touched the surface of these difficulties. This paper provides a unique solution for the authentication of data by using hyperledger fabric. The fog layer store data transferred by the IoT layer and calculate the hash value. These hash values are now stored in hyperledger fabric for authentication purposes. The proposed model results compared with lewako’s and Fan’s scheme and found that the proposed model has 25.00 % less encryption time, 09.3 % less decryption time, 17.48 % less storage overhead, and 23.38 % less computation cost as compared to Fan’s scheme.

A Combination Of A Rail Fence Cipher And Merkle Hellman Algorithm For Digital Image Security

Image is a combination of planes, points, lines and colors to create a physical or human object. Images can be in the form of 2-dimensional images, such as photographs and paintings. 3-dimensional image like a statue. The use of image media information has several weaknesses, one of which is the ease with which it can be manipulated by certain parties with the help of increasingly developing technology. In this study, the Rail Fence Cipher and Merkle Hellman methods were applied which aimed to obtain a stronger cipher by utilizing two key levels where an asymmetric algorithm was used to protect the symmetric key. The asymmetric algorithm used is Merkle Hellman and the symmetrical algorithm used is Rail Fence Cipher. The results of this study indicate that applying the Rail Fence Cipher and Merkle Hellman algorithms can secure image files and secure keys for data integrity. Encryption and description processing time is affected by the size and resolution of the image file.

A lattice‐based blind ring signature scheme for sensitive data protection in blockchain applications

SummaryEvery transaction is made public and verified by a third party, and the transparency of blockchain applications leads to privacy leaks. Everyone can access the transaction, making it easier for network analysis to identify the user's identity, which is a major problem with the blockchain. Financial institutions have become hesitant to adopt blockchain technology; as a result, preventing its spread. To confirm that encryption and security are maintained for transactions in the blockchain, this research introduces a zero‐knowledge proof (ZKP) based transaction validation scheme. To increase the security of blockchain applications, the proposed lattice‐based blind ring signature (LBRS) scheme integrates blind and ring signatures and is applied to sensitive data. The data are encrypted using this encryption scheme before being stored with the transactional information in the blocks. The encrypted data is then stored in the blocks, which users can retrieve after verification. The encrypted data is then stored in blocks, which users can retrieve after verification. A verification scheme based on the ZKP is used to prevent unauthorized access and changes to the data. The proposed LBRS scheme meets the security analysis requirement. The performance of an LBRS scheme is compared to other relevant models in terms of execution time, encryption time, and decryption time. Overall, the results are more encouraging and reassuring than other relevant works of interest. Finally, this model provides better computational security and satisfies the requirements like correctness and security analysis.

Maximizing blockchain security: Merkle tree hash values generated through advanced vectorized elliptic curve cryptography mechanisms

SummaryCloud computing is considered as the most fabulous paradigm to accommodate various kinds of user information. However, the privacy conflicts integrated with computational complexities need more hybridized resource efficiency for hassle‐free accession. For this purpose, this article presents a cloud assisted, secured and privacy preserved protocol based on the elliptical curve cryptography (ECC) and blockchain consortium. Blockchain provides an innovative approach for storing information, establishing trust, and various other transactions in an open platform. There exists no single technology as a panacea to obtain optimum privacy and security in the complex cloud environment that needs several desired characteristics. Hence, an elite integration of multiple cryptographic methods by carefully analyzing the potential harms and pitfalls has to be framed to balance the trade‐off between privacy and security. The proposed technique highlights the user privacy preservation through guaranteeing that user information is safeguarded from unauthorized use or access. In accordance with that the present study enrolled the advantages of the ECC algorithm, vectorization, and blockchain methods to rule out the limitations of state of art methods. This article attempts to provide the most required privacy with optimum key generation, encryption, and decryption time. The present study has obtained optimal outcomes and the maximum percentage reduction w.r.t the existing method in key generation time, encryption time, and decryption time is given as 14.89%, 16.67%, and 12.5%, respectively.

Secure MapReduce Framework with Data Compression Model to Evaluate Ideological Evaluation Contribution on Students

Ideological education is the process of transmitting and instilling a particular set of beliefs, values, and worldviews in individuals, with the goal of shaping their attitudes, behaviors, and perceptions. In colleges, ideological education plays an important role in shaping students' values, beliefs, and attitudes toward society, politics, and culture. The primary objective of ideological education in colleges is to promote critical thinking skills, social responsibility, and civic engagement among students. However, the use of big data in ideological education also raises concerns about privacy and ethical considerations. It is important to ensure that students' personal information is kept secure and that data is only used for educational purposes. It is also important to ensure that the use of big data does not lead to bias or discrimination of certain students or groups based on their demographics or backgrounds. This paper constructed a framework of Stream Cipher MapReduce Fractal Index (ScMFI). The ScMFI model performs the processing of information about students in ideological education in colleges. Through the Fractal Index Tree data is compressed with the MapReduce Framework. Finally, the ScMFI model uses the Stream Cipher process for data processing and storing in the cloud environment. The developed ScMFI model contribution of ideological education toward students’ awareness is classified as a machine learning model. The simulation analysis stated that ScMFI model minimizes the data storage, encryption and decryption time.

BLOCKCHAIN-BASED ZERO KNOWLEDGE PROOF MODEL FOR SECURE DATA SHARING SCHEME IN A DISTRIBUTED VEHICULAR NETWORKS

The possibility of implementing advanced applications, such as improved driving safety, has increased with the rapid development of vehicular telematics, and existing vehicular services have been enriched through data sharing and analysis between vehicles. This research uses smart contracts and consortium blockchain zero knowledge proof to secure data sharing and storage in vehicular networks. The results indicate that, for message sizes (m), both data_ experiments _2 and 1 produce ciphertext of the same size 157 bits, with the exception of 'gnfuv-temp-exp1-55d487b85b-5g2xh,' which generates ciphertext of 156 bits with the lowest decryption time of 26,865ms and a small decrease in encryption time between 28,620ms and 28,162ms. the proposed model validation shows that the model performed better than the Advanced encryption standard in terms of ciphertext size, encryption time and decryption time in comparison and it satisfies the good and robust blockchain-based zero knowledge proof model for secure data sharing and storage for distributed VANET. The scheme achieves high levels of security while operating with reasonable efficiency, reliability and availability according to numerical results.

Performance Analysis of Two Famous Cryptographic Algorithms on Mixed Data

The rapid development of digital data sharing has made information security a crucial concern in data communication. The information security system heavily relies on encryption methods. These algorithms employ strategies to increase data secrecy and privacy by obscuring the information, which only those parties who have the accompanying key can decode or decrypt. Nevertheless, these methods also use a lot of computational resources, including battery life, memory, and CPU time. So, to determine the optimal algorithm to utilize moving forward, it is necessary to assess the performance of various cryptographic algorithms. Therefore, this study evaluates two well-known cryptographies (RSA and ElGamal) using mixed data such as binary, text, and image files. CPU internal clock was used to obtain the time complexity used by both algorithms during encryption and decryption. The algorithms used CPU internal memory to obtain memory usage during the encryption and decryption of mixed data. Evaluation criteria such as encryption time, decryption time, and throughput were used to compare these encryption algorithms. The response time, confidentiality, bandwidth, and integrity are all factors in the cryptography approach. The results revealed that RSA is a time-efficient and resourceful model, while the ElGamal algorithm is a memory-efficient and resourceful model.

A Dynamic Image Encryption Scheme Based on Quantum Walk and Chaos-Induced DNA

The development of quantum information technology and increasing attention of people to the secure transmission of image information in the Internet have put forward higher requirements for traditional image encryption algorithms that not only take advantage of the exponential acceleration ability of quantum computing compared with classical computing but also reduce the risk of encryption algorithms being cracked. Therefore, in order to seek the combination of the advantages of quantum computing and classical image encryption algorithms, this paper proposes a new dynamic encryption image scheme of quantum walk and chaos-induced DNA. Firstly, the RGB three-channel pixels of the color image are extracted and combined into a one-dimensional array, and a random sequence is generated by quantum walk to reorder it to obtain a preliminary scrambled image; secondly, the color image is processed by the SHA-256 algorithm and divided into the generated message digest as the initial condition of the chaotic model. The random sequence was generated by the high-dimensional chaotic model which encodes each pixel independently and disorderly as DNA bases. The difference of the chaotic sequence ensures the dynamic selection of random DNA encoding and decoding rules during encryption. At the same time, the number of times of DNA encryption of the encoded pixel value is also controlled by the dynamic induction of the chaotic sequence, and ultimately, the DNA coding sequence is replaced with the decimal pixel value to obtain the encrypted image. The simulation results show that the information entropy of the encrypted image is above 7.99, and the correlation of each channel is close to 0, which can effectively resist brute force attacks, plaintext attacks, statistical analysis attacks, noise attacks, etc. In addition, in this paper, extracting the watermark embedded in the encrypted image to judge whether image information is tampered or forged further improves the security of the image information.

Perbandingan algoritma kriptografi simon dan vigenere dalam mengamankan citra digital

Digital image files or images are sometimes a valuable asset. Digital images that are private and confidential are very vulnerable to interception by other parties, especially if the image is distributed via the internet. To increase the security of digital images so that their confidentiality can be maintained, a special technique is needed to protect digital image messages, namely with cryptographic techniques. This study aims to determine the performance of the SIMON algorithm for digital image security. SIMON algorithm performance results are compared with the vigenere cipher algorithm in terms of time and image file size produced. In this study used base64 encode for the encryption process and base64 decode for the decryption process. The performance of the SIMON algorithm in securing digital images results in an average encryption time of 969 ms and an average decryption time of 1537 ms. The SIMON algorithm requires a longer time for the encryption and decryption process when compared to the Vigenere algorithm. The cipher image encrypted by the SIMON algorithm has a size larger than the original file by 36%. However, when compared to the cipher image encrypted by the Vigenere algorithm, there is no significant difference. The UACI value obtained from the SIMON algorithm cipher image obtained an average yield of 18.94%. Based on the theory of differential analysis, it can be said that this value is still vulnerable to differential attack. This is based on the UACI value which has not met the minimum threshold value of 33%.

Floyd–Warshalls algorithm and modified advanced encryption standard for secured communication in VANET

Vehicular Ad Hoc Networks (VANETs), which have the strategic goal of ensuring service delivery on roads and smart cities, are made up of a number of critical components, including the integration and communication between vehicles, sensors, and stationary road-side components. VANETs have many distinctive characteristics, including quick movable nodes, self-organization, scattered networks, and often changing topologies. Since attack prevention is still a contentious topic, security, data integrity, and user private information continue to be major concerns despite the recent development of VANETs. In particular, detecting Sybil attacks, in which one node attempts to appear as many, seemingly conflicts with the goal of privacy preservation, and existing schemes fail on either one or both accounts. In order to prevent Sybil attacks, VANETs assert that they have security measures in place. A new framework for VANET security and sybil attack detection was developed in this study. It groups the cars together using Kernel k-harmonic means (KKHM). Kernel functions are here included into the k-means problem's cost function via kernelizing in the feature space. Cluster centres are believed to exist in a higher level Hilbert space called the feature space. Using the Floyd-Warshall algorithm (FWA), cluster heads are chosen. The Deep Neural Network (DNN) is used to identify the malicious CH by extracting the necessary information from the CH. For the purpose of optimizing DNN parameters, a new technique known as gradient-based elephant herding optimization (GBEHO) is presented. To initialize the population in the search space, the gradient-based method GBO is paired with the elephant optimization algorithm (EHO). Second, by including Gaussian chaos mapping, the initialized population is enhanced in order to correct the disparity between the first exploration and utilization of the EHO. Additionally, two operators are programmed to modify the agents' position update strategy: the random wandering and variation operators. Last but not least, the Modified Advanced Encryption Standard (MAES) is used to send the data in the CH safely to the cloud. According on experimental findings, the suggested model increases the security rate of 96% and reduces the encryption time of 19(s) for 100 (kb) data which is better when compared to other existing models.

Proposed Hybrid Cryptosystems Based on Modifications of Playfair Cipher and RSA Cryptosystem

Cipher security is becoming an important step when transmitting important information through networks. The algorithms of cryptography play major roles in providing security and avoiding hacker attacks. In this work two hybrid cryptosystems have been proposed, that combine a modification of the symmetric cryptosystem Playfair cipher called the modified Playfair cipher and two modifications of the asymmetric cryptosystem RSA called the square of RSA technique and the square RSA with Chinese remainder theorem technique. The proposed hybrid cryptosystems have two layers of encryption and decryption. In the first layer the plaintext is encrypted using modified Playfair to get the cipher text, this cipher text will be encrypted using squared RSA to get the final cipher text. This algorithm achieved higher security to data but suffers from a long computational time. So Chinese remainder theorem has been used in the second hybrid cryptosystem to obtain less encryption and decryption time. The simulation results indicated that using the modified Playfair with the proposed square RSA has improved security. Moreover, using the Chinese remainder theorem achieved less encryption and decryption time in comparison to our first proposed and the standard algorithms.

An efficient and secure key management with the extended convolutional neural network for intrusion detection in cloud storage

SummaryCloud computing aids users for storing and recovering their information everywhere in the world. Security and efficiency are the two main issues in cloud service. Numerous intrusion detection techniques for the cloud computing environment were proposed, but those techniques do not effectively and accurately detect the attacks. Hence, an efficient and secure key management using extended convolutional neural network, that is, hybrid Enhanced Elman Spike convolutional Neural Network optimized with improved COOT optimization algorithm (Hyb EESCCNN) is proposed for intrusion detection in cloud system. Furthermore, the novel Adaptive Tangent Brakerski‐Gentry Vaikuntanathan Homomorphic Encryption (ATBGVHE) method is proposed for providing the security of the system. At first, SHA‐512 is used for authenticating cloud users to store its own information in to the cloud server. Then for Intrusion Detection (ID) the input data from the NSL‐KDD, UNSWNB15, CICIDS2018, and ToN‐IoT datasets are pre‐processed. The most relevant features are extracted using Fast Independent Component Analysis (Fast ICA) from the pre‐processed output. These extracted data are classified into malicious and non‐malicious data using Hyb EESCCNN. After classification, the non‐malicious data is secured using an ATBGVHE technique. The outcomes of the proposed methods shows that the NSL‐KDD datasets attains 99.9% higher accuracy, UNSW‐NB15 datasets offer 99.89% higher accuracy, CSE‐CIC‐IDS2018 datasets attains 99.8% higher accuracy, ToN‐IoT datasets attains 99.8% higher accuracy and 0.02 s lower encryption time compared with existing methods. Finally, case study with real time applications is also analyzed to prove the efficiency of the proposed method.

A Chunked and Disordered Data Privacy Protection Algorithm: Application to Resource Platform Systems

This paper provides a systematic analysis of existing resource platforms, evaluating their advantages and drawbacks with respect to data privacy protection. To address the privacy and security risks associated with resource platform data, we propose a novel privacy protection algorithm based on chunking disorder. Our algorithm exchanges data within a specific range of chunk size for the position and combines the chunked data with the MD5 value in a differential way, thus ensuring data privacy. To ensure the security of the algorithm, we also discuss the importance of preventing client and server decompilation during its implementation. The findings of our experiments are as follows. Our proposed privacy-preserving algorithm is extremely secure and easy to implement. Our algorithm has a significant avalanche effect, maintaining values of 0.61–0.85, with information entropy being maintained at 4.5–4.9. This indicates that our algorithm is highly efficient without compromising data security. Furthermore, our algorithm has strong encryption and decryption time stability. The key length can be up to 594 bits, rendering it challenging to decrypt. Compared with the traditional DES algorithm, our algorithm has better security under the same conditions and approaches the levels of security offered by the AES and RC4 algorithms.

Efficient Secure Communication in Zigbee Network Using the DNA Sequence Encryption Technique.

Zigbee IoT devices have limited computational resources, including processing power and memory capacity. Therefore, because of their complicated computational requirements, traditional encryption techniques are inappropriate for Zigbee devices. Because of this, we proposed a novel, "lightweight encryption" method (algorithm) is based on "DNA sequences" for Zigbee devices. In the proposed way, we took advantage of the randomness of "DNA sequences" to produce a full secret key that attackers cannot crack. The DNA key encrypts the data using two operations, "substitution" and "transposition", which are appropriate for Zigbee computation resources. Our suggested method uses the "signal-to-interference and noise ratio (SINR)", "congestion level", and "survival factor" for estimating the "cluster head selection factor" initially. The cluster head selection factor is used to group the network nodes using the "adaptive fuzzy c-means clustering technique". Data packets are then encrypted using the DNA encryption method. Our proposed technique gave the best results by comparing the experimental results to other encryption algorithms and the metrics for energy consumption, such as "node remaining energy level", key size, and encryption time.

A Lightweight Cryptographic Algorithm Based on DNA Computing for IoT Devices

Internet of Things (IoT) applications are used in almost every part of our life, so it is important to protect the sensitive data and information that is transmitted over wireless networks such as images and documents. The IoT devices have limited computational resources; they are called limited devices due to their limited processors and memory size. Traditional encryption methods require a lot of computing power; therefore, it is difficult to implement traditional cryptographic algorithm on IoT processor. Finally, a new, lightweight encryption method based on the DNA sequence is proposed to suit the IoT devices in a way to make an easy and secure the communications among the IoT devices. DNA sequences are very random, so we have used it to make a strong secret key that is hard for attackers to break. The proposed method has an advantage in terms of efficiency and strength. Experiments and security tests show that the proposed encryption system not only has a good encryption effect and can withstand known attacks, but it is also fast enough for real-world use. The DNA key is used to encrypt files using two simple and reliable methods such as substitution and transposition procedures that meet IoT computational requirements. In addition, when compared with other encryption algorithms, the experimental results shows that the key size, encryption time, and distortion preparation are all superior.

Enhancing Organizational Data Security on Employee-Connected Devices Using BYOD Policy

To address a business need, most organizations allow employees to use their own devices to enhance productivity and job satisfaction. For this purpose, the Bring Your Own Device (BYOD) policy provides controllable access for employees to organize data through their personal devices. Although the BYOD practice implies plenty of advantages, this also opens the door to a variety of security risks. This study investigates these security risks and proposes a complementary encryption approach with a digital signature that uses symmetric and asymmetric algorithms, depending on the organization’s digital certificate, to secure sensitive information stored in employees’ devices within the framework of BYOD policies. The method uses Advanced Encryption System (AES), Blowfish, RSA and ElGamal with a digital signature to achieve strong encryption and address critical security considerations such as user authentication, confidentiality and data integrity. The proposed encryption approach offers a robust and effective cryptographic solution for securing sensitive information in organizational settings that involve BYOD policies. The study includes experimental results demonstrating the proposed approach’s efficiency and performance, with reasonable encryption and decryption times for different key and file sizes. The results of the study revealed that AES and Blowfish have the best execution time. AES has a good balance of security and performance. RSA performs better than ElGamal in encryption and signature verification, while RSA is slower than ElGamal in decryption. The study also provides a comparative analysis with previous studies of the four encryption algorithms, highlighting the strengths and weaknesses of each approach.

Trust aware clustering based secure routing techniques in wireless sensor network

Wireless Sensor Networks are becoming increasingly popular in everyday life since they offer a variety of network structures for developing cutting-edge real-time applications. Wireless sensor devices have high energy consumption limitations, so it is necessary to handle excessive energy consumption by malicious nodes properly to improve network performance. Even though numerous studies have been conducted to increase the dependability of routing in WSNs, the existing routing strategies do not meet the required security constraints by using intelligent methods to protect the sensor nodes from malicious attack. To overcome this challenge a novel Trust Aware Clustering based Secure Routing Techniques (TAC-SRT) has been proposed to minimize the overall energy consumption, improved security to nodes and to maximize the network lifetime. The proposed method is carried out in three phases. In the first phase, the cluster head is selected by using K mean clustering. In the second phase, the trust value of each node is evaluated by using Mamdani fuzzy inference rule. In the third phase, the Tversky similarity index is used to find the normal or malicious node and establishes the shortest route. The Fully Homomorphic Elliptic Curve Cryptography technique is then used to perform secure data transmission. The effectiveness of the proposed strategy is examined using several parameters, such as the lifetime of the network, data confidentiality, active nodes, and energy consumption. The proposed technique improves the network lifetime by 23.01%, 17.4%, and 13.2% better than MOSFA, SecDL, and CAR-MOSOA respectively. Finally, the proposed method demonstrated superior performance in terms of delay, throughput, encryption time, network lifetime, and packet delivery ratio compared with existing techniques.

A Secure and Energy-efficient Framework for Air Quality Prediction Using Smart Sensors and ISHO-DCNN

Background: The World Health Organization (WHO) reported that Air pollution (AP) is prone to the highest environmental risk and has caused numerous deaths. Polluted air has many constituents where Particulate Matter (PM) is majorly reported as a global concern. Currently, the most crucial challenges faced by the globe are the identification and treatment of augmenting AP. The air pollution level was indicated by the Air Quality Index (AQI). It is affected by the concentrations of several pollutants in the air. Many pollutants in the air are harmful to human health. Thus, an efficient prediction system is required. Many security problems and lower classification accuracy are faced by them even though several prediction systems have been formed. A secure air quality prediction system (AQPS) centered upon the energy efficiency of smart sensing is proposed in this paper to overcome these issues. From disparate sensor nodes, the input data is initially amassed in the proposed work. The gathered data is stored in the temporary server. Next, the air-polluted data of the temporary server is offered to the AQPS, wherein preprocessing of the input data along with classification is executed. Methods: Utilizing the Improved Spotted Hyena Optimization-based Deep Convolution Neural Network (ISHO-DCNN) algorithm, the classification is executed. Utilizing the Repetitive Data Coding Based Huffman Encoding (RDC-HE) method, the polluted data attained from the classified output is compressed and encrypted by employing the American Standard Code for Information Interchange based Elliptical Curve Cryptography (ASCII-ECC) method. Results: Afterward, the encrypted and compressed data is saved in the Cloud Server (CS). Finally, for notifying about the AP, the decrypted and decompressed data is offered to the Base Stations (BS). Conclusion: The proposed work is more effective when analogized to the prevailing methods as denoted by the experimental outcomes. Higher accuracy of 97.14% and precision of 91.44% were obtained by the proposed model. Further, lower Encryption Time (ET) and Decryption Time (DT) of 0.565584 sec and 0.005137 sec were obtained by the model.

Efficient and Secure Certificateless Signcryption Without Pairing for Edge Computing-Based Internet of Vehicles

The emergence of the vehicular ad-hoc network (VANETs) could meet the need for an efficient and safe transportation. Vehicles wirelessly interact with nearby road-side units (RSUs) and send the data to the cloud servers (CS), but this approach has limitations (i.e. network congestion and privacy leakage). Edge computing can effectively solve problems of network congestion, feedback delay and privacy leakage during transmission caused by CS. Thus, this paper proposes a certificateless aggregate signcryption with online/offline encryption (CLOASC) based on edge computing (EDC). Correspondingly, certificateless signcryption (CLSC), online/offline encryption (OOE) and aggregate signcryption technology solve the key escrow problem, shorten the encryption time and realize the quick verification of multiple users, respectively. Furthermore, it is worth mentioning that the scheme we propose does not have any bilinear pairings or scalar multiplications in the online encryption stage, which greatly reduces the computational cost in the online stage. In the random oracle model, our scheme was proved to be existential unforgeability under adaptive chosen message attacks (EUF-ACM) and adaptive indistinguishability under chosen-ciphertext attack (IND-CCA2) security. Simulation results show that our scheme is computationally and communication based lightweight as compared to the related.