Optimal Integrity Policy for Encrypted Data in Secure Storage using Cloud Computing

Cloud computing technologies comprise various kinds of significant desirable constrains such as security, liability, government surveillance, telecommunications capacity, anonymity and privacy. The usage of cipher text technology is considered as a desired technique for performing the process of encryption in order to solve the issue of granting security to the data that are shared in the cloud. Similarly, the architecture of multi-tenant in the cloud computing system grants benefits to both the service providers and end-users which shares a common cloud platform to multiple tenants (i.e.) users and suitable resources are also computed by implementing proposed architecture. Therefore in this research work, the concept of cipher text multi-tenant are integrated for providing enhanced security to the data shared in the cloud environment. Hence a Modified Elliptic Curve Cryptography (MECC) based on Diffee Hellman algorithm is proposed in this research paper which provides enhanced security using alternate key generation. The encryption, decryption, upload and download time are calculated and it is concluded that the algorithm that is proposed in this research paper consumes less time for all these measures when comparing with other existing algorithms. Characteristics like less memory, high operational performance, small sized keys and rapid key generation process, and effective resource savings enable the modified ECC to obtain high efficiency. The encryption time of the proposed MECC system was 51ms with the key length 4096 bits, whereas existing method had 92ms as encryption time. Likewise With the key length of 4096 bits, the decryption time for the proposed model was 159ms. Accordingly the proposed system has reduced cipher text key size of 836KB when compared with the existing AES, Blow Fish, and Two Fish. Additionally, the key generation time (35 s) also seems to be considerably reduced when compared with the existing methods. These statements reveal that the proposed system outshine the state of art methods in terms of key generation time, encryption and decryption time and computational complexity.

Enabling a security and privacy preservation for the cloud data is one of the demanding and crucial tasks in recent days. Because, the privacy of the sensitive data should be safeguard from the unauthorized access for improving its security. So, various key generation, encryption and decryption mechanisms are developed in the traditional works for privacy preservation in cloud. Still, it remains with the issues such as increased computational complexity, time consumption, and reduced security. Also, the traditional works use the symmetric key cryptography based. Thus, this paper aims to develop a new privacy preservation mechanism by implementing a fully homomorphic–elliptic curve cryptography (FH-ECC) algorithm. The data owner encrypts the original data by converting it into the cipher format with the use of ECC algorithm, and applies the FH operations on the encrypted data before storing it on the cloud. When the user gives the data request to the cloud, the Cloud Service Provider verifies the access control policy of the user for enabling the restricted access on the data. If the access policy is verified, the encrypted data is provided to the user, from that the cipher text is extracted. Then, the ECC decryption and FH operations are applied to generate the original text. Based on the several analysis, the research work is evaluated with the help of different performance measures such as execution time, encryption time, and decryption time. In addition the effectiveness of the novel FHE technique is justified by the comparative analysis made with the traditional techniques.

 Abstract—Cloud computing is an emerging technology that is still unclear to many security problems. Ensuring the security ofstored data in cloud servers is one of the most challenging issues in such environments. Accordingly, this schema presents a Hybrid Encryption algorithm that is based on RSA Small-e and Efficient RSA (HE-RSA) for improving the reliability in cloud computing environments. The main aim of this project is to use the cryptography concepts in cloud computing communications and to increase the security of encrypted data in cloud servers with the least consumption of time and cost at the both of encryption and decryption processes.In the proposed model, the number of key generation exponents has been increased in comparison to the original RSA. Moreover,a dual encryption process has been applied in this algorithm to prevent common attacks against RSA algorithm. In addition, this schema also presents the comparison between HE-RSA and original RSA in terms of security and performance. For this purpose, Key Generation, Encryption and Decryption Time in Original RSA and HE-RSA have been compared according to the different size of exponents. Moreover, some of the common attacks against RSA algorithm have been analysed to detect the resistance of the proposed algorithm against possible attacks. Finally, the effects of using HE-RSA on cloud computing environments have been reviewed in more depth and details.

Most cloud users and officialdoms have a great interest in adoption and use cloud services like cloud storage to store their data on cloud environment. With the speedy growing in cloud computing technology, user's data security and privacy in cloud computing technology is the utmost dangerous issues because data owner stores their sensitive data onto remote cloud storage. However, successful retrieval of confidential data and computation on confidential data is hard to succeed by traditional cryptographic algorithm. Hence, to warrant the cloud security we are using the cryptographic algorithms that supports for computation over encrypted data. In this, Homomorphic cryptographic algorithm is one such technique that has interested homomorphic cryptographic algorithm used to secure user data in cloud server. It performs computation on cipher texts data can be operated directly without affecting the confidentiality of the encryption algorithms. We have established a framework for security of data on a cloud storage by using fully homomorphic cryptographic algorithm. Indeed, In this study, we have evaluated complexity of three homomorphic cryptographic algorithm those are SDC FHE, Paillier and RSA with encryption time, decryption time, throughput and memory usage

Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and motivation. In this work, we present an IoT-Based E-Learning testbed using Raspberry Pi mounted on Raspbian. We carried out some experiments with a student of our laboratory for brain waves and, encryption and decryption time of cipher algorithms. We used MindWave Mobile (MWM) to get the data. For experimental results, when using our system the concentration is 80 [%] from 0 [s] to 400 [s]. But, for 400 [s] to 1000 [s], concentration is decreased. In the case of not using our system, concentration is almost the same (about 40 [%]) from 0 [s] to 1000 [s]. From simulation results, we found that the decryption time is faster than encryption time, however for 3-DES and Raspberry Pi decryption time is higher than encryption time.

The Data Integrity Based Secure Cloud Data Outsourcing Model involves designing a framework that ensures data outsourced to the cloud remains secure, intact, and authentic. Cloud computing has become an indispensable resource for businesses and individuals alike, providing scalable and cost-effective solutions for data storage and management. However, outsourcing data to external cloud service providers (CSPs) raises serious questions around data security, integrity, and authenticity. It is essential to retain confidence and comply with regulatory criteria to make sure that outsourced data is authentic, obtainable, and unaltered. In this article, the Low-Complexity Elliptic Galois Cryptography based Secure Data Integrity Verification Scheme for Cloud Storage (LCEGC-SDIV-CS) is proposed. Initially, the integration of the advantages in Trusted Execution Environment (TEE) and identity-based encryption using Low-Complexity Elliptic Galois Cryptography (LCEGC) is provided to create a safe data integrity verification scheme. Then, data integrity using Block Access Signature Oriented Merle Hash Tree (BS-MHT) is used for the integrity-preserving structure for encrypted block-oriented storage systems. The random Oracle model's security analysis, depending upon the computational Diffie-Hellman assumption, provides security by preventing attacks when the adversary chooses the messages with targets’ identities. The proposed LCEGC-SDIV-CS model is implemented and the performance metrics like Data uploading with downloading time, Memory usage on data uploading with downloading, Encryption time, Decryption time, and Security levels are examined. The proposed LCEGC-SDIV-CS method attains 21.56%, 23.98%, and 21.19% lower data uploading with downloading time 24.78%, 23.82%, and 23.52% lower encryption time are analyzed with existing methods EDI-SPD-SCS, NSA-SADS-BDEPDP-SDS-RCS respectively.

Smart grid is an advanced electrical infrastructure that incorporates communication and information technologies to enable power firms and consumers to exchange data in bidirectional. However, there are serious security risks associated with relying on an unsecured open network. To overcome the drawbacks, an efficient HEAAN-PUF based IoT device authentication with SWO-QBFT for smart grid infrastructure has been proposed. Initially IoT device is registered and authenticated using Physical Unclonable Function (PUF) authentication, which provides a unique response for each device with an Interplanetary File System (IPFS). Homomorphic Encryption for Arithmetic of Approximate Numbers (HEAAN) cryptography algorithm was then utilized to generate the key for encrypting the data and stored in IPFS. Blockchain based on consensus algorithm as Spider Wasp Optimization (SWO) with Quorum Byzantine Fault Tolerance Protocol (SWO-QBFT) utilized to choose the best validators for secure data transmission and stored an IPFS hash address. Performance metrics-based analysis and comparison of the proposed HEAAN-OQBFT technique with existing approaches are conducted. Performance metrics like encryption and decryption times, key and block generation times, throughput, Packet Delivery Ratio (PDR), and Packet Loss (PL) are acquired in order to determine the performance of the proposed method. Time required for Block creation, encryption, decryption, and key generation takes up 0.19 ms, 0.013 ms, 0.023 ms and 0.93 ms with is 497 Kbps throughput, 1% packet loss, and 99% PDR. The metrics therefore clearly show that the proposed method successfully stores and secures smart grid data by integrating OQBFT with an effective HEAAN cryptography.

The development of digital world created advancement of technology, by which the data services are electronically transacted. Electronic voting is used to improve the election system in several ways. But technical issues associated with protection of data, is considered as a major concern leading to illegal activities and threats. Some of the conventional methods used in securing e-votes are matching finger prints of individual, control unit accessibility, which provides decreased capability of attaining proper authenticity and security. Even several cryptographic methods are used for data security, but produced limited accuracy. So, to improve the reliability and accuracy of data protection in e-voting, combination of AES (Advanced Encryption System) and RSA (Rivest, Shamir, Adleman) combined with blockchain technology is implemented. AES uses larger key size which makes the data privacy robust. But encryption and decryption time produces decreases computational speed. So, AES is combined with RSA, as it uses shorter key length and easy to implement with block-chain algorithm, it is deployed to decrease encryption and decryption time, thus producing highly sensitive data security. The performance of the system is validated by calculating the encryption and decryption time, block size and verification time of original data with output data.

WITHDRAWN: Efficient Secure Aware Scheduling Model for Enhancing Security and Workflow Model in Cloud Computing

The current privacy protection scheme of medical electronic health records ignores the security of role interaction, which leads to low security of medical electronic health record data and a long time for data encryption and decryption. This paper proposes a privacy protection method of medical electronic health records based on blockchain and asymmetric encryption. This paper uses blockchain technology and asymmetric encryption algorithm to establish an electronic health record system model, selects the alliance chain to control the permissions of alliance members, constructs the corresponding sub key, uses shared users for information interaction, obtains the file key through verification (so as to decrypt the data and obtain the shared data plain text), and realizes medical electronic health through the privacy and protection scheme process of health records. Experimental results show that the data security of the proposed method is as high as 92 %, and the encryption and decryption time is only 0.45 s, which can effectively improve the data security and shorten the data encryption and decryption time.

Data Migration is a multi-step process that begins with analyzing old data and culminates in data uploading and reconciliation in new applications. With the rapid growth of data, organizations constantly need to migrate data. Data migration can be a complex process as testing must be done to ensure data quality. Migration also can be very costly if best practices are not followed and hidden costs are not identified in the early stage. On the other hand, many organizations today instead of buying IT equipment (hardware and/or software) and managing it themselves, they prefer to buy services from IT service providers. The number of service providers is increasing dramatically and the cloud is becoming the preferred tool for more cloud storage services. However, as more information and personal data are transferred to the cloud, to social media sites, DropBox, Baidu WangPan, etc., data security and privacy issues are questioned. So, academia and industry circles strive to find an effective way to secure data migration in the cloud. Various resolving methods and encryption techniques have been implemented. In this work, we will try to cover many important points in data migration as Strategy, Challenges, Need, methodology, Categories, Risks, and Uses with Cloud computing. Finally, we discuss data migration security and privacy challenge and how to solve this problem by making improvements in it’s using with Cloud through suggested proposed model that enhances data security and privacy by gathering Advanced Encryption Standard-256 (ATS256), Data Dispersion Algorithms and Secure Hash Algorithm-512. This model achieves verifiable security ratings and fast execution times.

The security of data transmission over the Passive Optical Network (PON) is a very challenging task. In this research work, joint secure compressive sensing encryption and symbol scrambling method is introduced in the Generalized Frequency Division Multiplexing (GFDM) systems to overcome this problem. After compression, CS data is converted into Binary Phase Shift Keying (BPSK) symbols, the SHA-256 technique is used for key generation purposes, and the key selection process is performed to improve the high security of the proposed system. Ebola meta-heuristic scheme is proposed for the optimal key selection process. The maximum Peak-To-Signal Noise Ratio (PSNR) value is the Objective Function (OF) of this proposed key selection to choose the optimal secret key from the random numbers. Finally, the symbol scrambling function is performed on the resulting BPSK symbols to improve the security of the CS data. The performance of this proposed system is compared with some of the existing methods The achieved values of the proposed GFDM-PON for PSNR, MSE, Entropy, encryption time, and decryption time are 57.86319, 0.004114, 9.495201, 0.054, and 0.048, respectively.KeywordsPassive optical networkCompressed sensingBinary phase shift keyingKey selectionPeak to noise ratio

Cloud computing is currently emerging as a promising next-generation architecture in the Information Technology (IT) industry and education sector. The encoding process of state information from the data and protection are governed by the organizational access control policies. An encryption technique protects the data confidentiality from the unauthorized access leads to the development of fine-grained access control policies with user attributes. The Attribute-Based Encryption (ABE) verifies the intersection of attributes to the multiple sets. The handling of adding or revoking the users is difficult with respect to changes in policies. The inclusion of multiple encrypted copies for the same key raised the computational cost. This paper proposes an efficient Key Derivation Policy (KDP) for improvement of data security and integrity in the cloud and overcomes the problems in traditional methods. The local key generation process in proposed method includes the data attributes. The secret key is generated from the combination of local keys with the user attribute by a hash function. The original text is recovered from the ciphertext by the decryption process. The key sharing between data owner and user validates the data integrity referred MAC verification process. The proposed efficient KDP with MAC verification analyze the security issues and compared with the Cipher Text–Attribute-Based Encryption (CP-ABE) schemes on the performance parameters of encryption time, computational overhead and the average lifetime of key generation. The major advantage of proposed approach is the updating of public information and easy handling of adding/revoking of users in the cloud.

Even though cloud computing has advanced over the years, the two major challenges currently faced by cloud computing are data security and trusted computing. In cloud computing applications, blockchain technology can enhance data security and trusted computing when paired with virtual machine agents and mobile agent technologies. A decentralized new framework for distributed computing is blockchain. A potential area of study is the integration of blockchain technology with cloud computing, leveraging the former's security mechanisms to enhance the latter's secure computing and storage capabilities. The usage of the proposed framework builds on the inherent security features of blockchain technology in an effort to secure data and information integrity, confidentiality and authentication in cloud environments. Through the introduction of distributed virtual machine agents, the framework increases the scalability and effectiveness of data protection mechanisms for virtual data environments that can be used to store information protected by cloud security measures. Thus, the proposed study substantiates the applicability and efficiency of the existing and emerging problems of cloud data security and this approach develops satisfactory data protection solutions.

The Internet of Medical Things (IoMT) is reforming healthcare by enabling interconnected medical devices and systems to facilitate efficient data collection, transmission, and analysis. While IoMT has significantly improved real-time monitoring and personalized care, the transmission of high-resolution medical images remains a challenge due to bandwidth constraints, latency issues, data loss, and computational overhead. Efficient and secure medical image transmission is critical to ensuring reliable diagnostics and timely patient care in this ecosystem. This research presents an optimized Deep Learning (DL) architecture developed to overcome the limitations of medical image transmission in IoMT environments. The proposed solution incorporates Convolutional Neural Networks (CNNs) for spatial feature extraction and dimensionality reduction while preserving diagnostic-critical information, and Long Short-Term Memory (LSTM) networks to manage sequential data and mitigate transmission issues such as packet loss and latency. The framework incorporates robust encryption mechanisms to ensure data security without significantly increasing computational overhead. Once predictions are made, the data is securely transferred to the cloud for further analysis and storage. Furthermore, Hippopotamus Optimization is utilised to enhance the model's performance and fine-tune hyperparameters, improving both efficiency and accuracy. Performance evaluations were conducted using real-world medical image datasets under varying IoMT network conditions. The results demonstrate that the proposed CNN-LSTM framework delivers superior performance across key metrics, like Peak Signal-to-Noise Ratio (PSNR), accuracy, F1 score, specificity, and sensitivity. Additionally, the framework optimizes encryption and decryption times and reduces bandwidth consumption, ensuring efficient and secure data transmission. This approach showcases a significant advancement in IoMT-based medical imaging, paving the way for enhanced reliability and efficiency in healthcare delivery systems.