About Fully Homomorphic Encryption Improvement Techniques

Fully homomorphic encryption schemes (FHE) are a type of encryption algorithm dedicated to data security in cloud computing. It allows for performing computations over ciphertext. In addition to this characteristic, a verifiable FHE scheme has the capacity to allow an end user to verify the correctness of the computations done by a cloud server on his encrypted data. Since FHE schemes are known to be greedy in term of processing consumption and slow in terms of runtime execution, it is very useful to look for improvement techniques and tools to improve FHE performance. Parallelizing computations is among the best tools one can use for FHE improvement. Batching is a kind of parallelization of computations when applied to an FHE scheme, it gives it the capacity of encrypting and homomorphically processing a vector of plaintexts as a single ciphertext. This is used in the context of cloud computing to perform a known function on several ciphertexts for multiple clients at the same time. The advantage here is in optimizing resources on the cloud side and improving the quality of services provided by the cloud computing. In this article, the authors will present a detailed survey of different FHE improvement techniques in the literature and apply the batching technique to a promising verifiable FHE (VFHE) recently presented by the authors at the WINCOM17 conference.

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

With the wide adoption of cloud computing paradigm, it is important to develop appropriate techniques to protect client data privacy in the cloud. Encryption is one of the major techniques that could be used to achieve this goal. However, data encryption at the rest alone is insufficient for secure cloud computation environments. There is also the need for efficient techniques to carry out computation over encrypted data. Fully homomorphic encryption (FHE) and garbled circuits are naturally used to process encrypted data without leaking any information about the data. However, existing FHE schemes are inefficient for processing large amount of data in cloud and garbled circuits are one time programs and cannot be reused. Based on quaternion/octonion algebra and Jordan algebra over finite rings \(\mathbb {Z}_q\), this paper designs efficient fully homomorphic symmetric key encryption (FHE) schemes without bootstrapping (that is, noise-free FHE schemes) that are secure in the weak ciphertext-only security model assuming the hardness of solving multivariate quadratic equation systems and solving univariate high degree polynomial equation systems in \(\mathbb {Z}_q\). The FHE scheme designed in this paper is sufficient for privacy preserving computation in cloud.

Fully homomorphic encryption (FHE) allows one to perform arbitrary computation on encrypted data. From the cloud computing perspective, a FHE scheme can be used to outsource a computation to a remote cloud server without revealing the computed data to the server. This is a very attractive cryptographic primitive. Existing methods used to construct FHE schemes are complicated, and often these methods are difficult to understand except by experts in the field. This paper aims to explain how to construct a FHE scheme in an easily understandable way. We first present a general overview of FHE scheme construction. We then take the van Dijk et al FHE scheme as an example and explain how a FHE scheme is obtained from a simple integer-based encryption scheme. Our explanation gives details of the noise bound, how noise growth can be controlled and how the vanDijk scheme is implemented.

All the organization wants to implement the same security parameters as they are already using with their internal data/resources. It is mandatory to understand and find the data protection challenges before outsourcing the data security in cloud computing. In present research we are discussing about the impact of security in cloud computing including all the challenges associated with it.

Cloud computing is the present technology which is gaining greater attentions. Nowadays, among all the technologies that includes the network security, data protections, virtualization security, application integrity and identity management, the cloud is the most essential and key aspect. Among all the above mentioned issues, data protection is one of the most important security element because organizations cannot transfer their data to the remote machines as there is no guarantee of data protection by the cloud service providers. Many techniques are already available to protect the data in cloud. Though there are many number of techniques implemented to have focus on security of data but still some challenges persist. Cloud computing solves not only the computing problems but also caters loads of data with software updates etc. The user will be confident in utilising the cloud sources only if there is an appropriate collaboration between cloud client and cloud service provider. The most popular security techniques include SSL (Secure Socket Layer) Encryption, Algorithms like AES, Blowfish, DES, RSA, Cloud Computing, and Data Security. This paper mainly focus on how to analyse and evaluate the most important security encryption algorithms for data protection in cloud computing. Furthermore, this paper also discusses about some of the new security techniques for data protection which will be recommended in order to have improved security in cloud computing. Keywords: Cloud, Data Protection, Security, Asymmetric Algorithms, Symmetric Algorithm.

Generally, enterprises store data in internal storage and establish firewalls to protect against intruders attempting to access the data. In cloud computing, data is stored in storage provided by service providers. Information privacy protection involves storing data in encrypted form. The cloud system is responsible for both storage and the encryption or decryption of data, allowing system administrators to access encrypted data and decryption keys. This capability poses a risk to information privacy as it enables unauthorized access to information. This survey discusses Data Storage in Cloud Computing, focusing on the concept of storing encryption and decryption services alongside the storage service. Additionally, the survey examines storage system architecture, security challenges, services, and user application systems to provide a comparison of various cloud models. In addition to the fundamental aspects of data storage and encryption, this survey delves into the evolving landscape of cloud computing, addressing the dynamic nature of storage system architectures. Enterprises increasingly rely on cloud service providers for scalable and flexible storage solutions, prompting a critical examination of the security challenges associated with this paradigm shift. The survey examines how storage services in different cloud models affect user applications and privacy. It stresses the importance of understanding cloud providers' services amidst evolving technology and growing threats. The study delves into trends like edge computing and distributed storage, evaluating their impact on cloud storage security. Overall, it aims to provide a comprehensive overview for enterprises and researchers to make informed decisions on data security in Cloud Computing.

<p>Cloud computing has spread widely among different organizations due to its advantages, such as cost reduction, resource pooling, broad network access, and ease of administration. It increases the abilities of physical resources by optimizing shared use. Clients’ valuable items (data and applications) are moved outside of regulatory supervision in a shared environment where many clients are grouped together. However, this process poses security concerns, such as sensitive information theft and personally identifiable data leakage. Many researchers have contributed to reducing the problem of data security in cloud computing by developing a variety of technologies to secure cloud data, including encryption. In this study, a set of encryption algorithms (advance encryption standard (AES), data encryption standard (DES), Blowfish, Rivest-Shamir-Adleman (RSA) encryption, and international data encryption algorithm (IDEA) was compared in terms of security, data encipherment capacity, memory usage, and encipherment time to determine the optimal algorithm for securing cloud information from hackers. Results show that RSA and IDEA are less secure than AES, Blowfish, and DES). The AES algorithm encrypts a huge amount of data, takes the least encipherment time, and is faster than other algorithms, and the Blowfish algorithm requires the least amount of memory space.</p>

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

SummaryCloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods.

Cloud computing is defined as the management and provision of resources, software, applications, and information as services over the internet. Numerous organizations are adopting cloud services because of the economy and technology changes. Over the past year, cloud computing has made life easier for other people or organizations by allowing people to have access to information from anywhere in the world through the internet. However, cloud computing introduces a series of security concerns, which data security is a major concern. Data security is basically linked with any security in the cloud. If the network or infrastructure security is compromised, the possibility there is a possibility of data being affected. Data security has three important principles that are Confidentiality, Integrity, and Availability (CIA) that needs to always be maintained. Understanding the security concerns in cloud computing is an important requirement for those who are planning to move to the cloud solution. Thus, this paper aims to investigate and develop a hybrid data security framework to mitigate data security challenges in the cloud, focusing on legal frameworks such as POPIA and GDPR. A survey was conducted to investigate which techniques are more effective in protecting data and the evaluation ensured that CIA triads are covered. The limitation on the existing techniques is that they address only one principle or two, but not all three at the same time. The best technique among the others will be used integrated when designing the proposed framework in order to address CIA. Having more than one security technique to address data security will improve the security in the cloud and will also bring more business to cloud service providers when customers see the security in data enhanced.

The development of intelligent algorithm in cloud computing network service can well fit the many nodes and links among cloud computing network service systems. Approximate calculation on reliability is especially important in network era of large-scale cloud computing. This thesis focuses on the date security and encryption in cloud computing and proposes the security strategy of updating dual Cache driven by time. Strategy based on Cache configuration and time division can be used to update Cache driven by time, so as to solve the SCA (side channel attacks) during cloud computing. The strategy for updating Cache driven by time is realized through random N-time cached updates for each user in time period T, aiming to disturb the attacker’s confirmation about whether Cache is hit and make the attacker use invalid information in side channel information process Thus, the safety data in Cache under cloud computing environment can be protected and users shall no longer worry about the data in cloud computing.

Ciphertext-policy attribute-based encryption (CP-ABE) is becoming a promising solution to guarantee data security in cloud computing. In this paper, we present an attribute-based secure data sharing scheme with Efficient revocation (EABDS) in cloud computing. Our scheme first encrypts data with Data encryption key (DEK) using symmetric encryption and then encrypts DEK based on CP-ABE, which guarantees the data confidentiality and achieves fine-grained access control. In order to solve the key escrow problem in current attribute based data sharing schemes, our scheme adopts additively homomorphic encryption to generate attribute secret keys of users by attribute authority in cooperation with key server, which prevents attribute authority from accessing the data by generating attribute secret keys alone. Our scheme presents an immediate attribute revocation method that achieves both forward and backward security. The computation overhead of user is also reduced by delegating most of the decryption operations to the key server. The security and performance analysis results show that our scheme is more secure and efficient.

Ponte Academic JournalJun 2017, Volume 73, Issue 6 SOA-BASED PROCEDURE WITH DYNAMIC SECURITY FOR ADAPTIVE CLOUD COMPUTING FRAMEWORKAuthor(s): Vasu Yadav Dokku ,V. Krishna ReddyJ. Ponte - Jun 2017 - Volume 73 - Issue 6 doi: 10.21506/j.ponte.2017.6.9 Abstract:Cloud computing is defined as a ondemand delivery of resources and applications whenever user wants and pay according to its usage. In the cloud data outsourcing and providing real-time data privacy are said to be important concepts to share the data to registered users only. Different cloud approaches was introduced to provide privacy for outsourced data, based on their limitations and advantages, a cloud computing adoption framework (CCAF) was introduced to provide dynamic and customized data privacy in the cloud. In this framework, they used business process modeling notation (BPMN) for data usage and data sharing and outsourcing data in distributed cloud computing. To identify general users and intruders using convergent encryption technique it is possible to provide data security in cloud computing. To provide third party service to outsourced business modeling then CCAF framework will fail to provide dynamic authentication in distributed computing. So in this paper, we propose and implement Novel Cross-Realm Privacy (NCRP) approach for collaborating Service-Oriented Architecture to various security states in outsourced data to provide multiparty business services for dynamic authentication. We improve the CCAF framework, by means of changing the process when outsourcing the data. Our experimental results give the implementation of proposed approach with dynamic authentication and describe the improved performance evaluation with the scalability of outsourced data in cloud computing framework. Download full text:Check if you have access through your login credentials or your institution Username Password

Cloud computing that provides elastic computing and storage resource on demand has become increasingly important due to the emergence of “big data”. Cloud computing resources are a natural fit for processing big data streams as they allow big data application to run at a scale which is required for handling its complexities (data volume, variety and velocity). With the data no longer under users’ direct control, data security in cloud computing is becoming one of the most concerns in the adoption of cloud computing resources. In order to improve data reliability and availability, storing multiple replicas along with original datasets is a common strategy for cloud service providers. Public data auditing schemes allow users to verify their outsourced data storage without having to retrieve the whole dataset. However, existing data auditing techniques suffers from efficiency and security problems. First, for dynamic datasets with multiple replicas, the communication overhead for update verifications is very large, because each update requires updating of all replicas, where verification for each update requires O(log n ) communication complexity. Second, existing schemes cannot provide public auditing and authentication of block indices at the same time. Without authentication of block indices, the server can build a valid proof based on data blocks other than the blocks client requested to verify. In order to address these problems, in this paper, we present a novel public auditing scheme named MuR-DPA. The new scheme incorporated a novel authenticated data structure (ADS) based on the Merkle hash tree (MHT), which we call MR-MHT. To support full dynamic data updates and authentication of block indices, we included rank and level values in computation of MHT nodes. In contrast to existing schemes, level values of nodes in MR-MHT are assigned in a top-down order, and all replica blocks for each data block are organized into a same replica sub-tree. Such a configuration allows efficient verification of updates for multiple replicas. Compared to existing integrity verification and public auditing schemes, theoretical analysis and experimental results show that the proposed MuR-DPA scheme can not only incur much less communication overhead for both update verification and integrity verification of cloud datasets with multiple replicas, but also provide enhanced security against dishonest cloud service providers.