Review of Data Security and Privacy in Cloud Computing

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.

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.

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.

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.

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 is the new bending curve in the line of Information Technology and computing paradigm. Cloud computing has simultaneously revolutionized business and government sectors by stretching its arms in every possible field, from not only to Information communication technology to medical advancements to a more diverse agriculture field which was untouched by technology for a decade. Cloud computing enlightens path for another dimension of computing which is expected to rise even more than mobile computing, i.e., Internet of Things (IOT) or internet of everything. As new dimensions are being added up to the cloud domain every day this gives a window to hackers and intruders to breach the doors. The security problem is amplified under cloud computing as it introduces new problem domains. One of the major problem domain identified is data security, where security of user’s data is the utmost priority. Providing privacy to the user and the data stored by the user is to be ensured by the cloud service provider. This paper aims at the security model for cloud computing which ensures the data security and integrity of user’s data stored in the cloud using cryptography.

Patient Health Record (PHR) is a complete Patient related health data, which is getting stored in cloud computing to gain cost benefit and better access control.In maintaining PHR, cloud computing plays an inevitable role, since small hospitals and physicians are not affordable to maintain own servers to keep PHRs for cost and security reasons .Providing accessibility to variable stake holders like Patients, Physicians, Hospitals and Insurance Providers, become a tedious process in private individual servers with encryption mechanisms.Cloud solution ensures that PHRs ' availability to the necessary stake holders at any point of time.In any country , there are laws which governs to maintain privacy of patient health records, and hence maintaining PHRs in cloud are subjected to privacy concerns and high risk of getting exploited.There are various encryption schemes exist to preserve PHR's security and privacy in Cloud computing.In this paper, we propose a secure PHR accessing scheme for dynamic environment to preserve privacy in semi trusted cloud servers .

This paper analyzes the current problems in the data security and privacy of cloud computing. On the basis, this paper mainly research about the legitimacy and validity of research cloud computing process. First of all, this paper understand the concept of cloud computing technology, according to the working principle of the structure model of the cloud computing service analyzes the existence of data security and privacy. Cloud computing service providers for cost savings, or application access layer has invasion of illegal users, so cloud computing task submitted by the legitimate user will not be executed or not executed completely, and the users will get that a random outcome, and task submitted by illegal users may be was carried out. Research on the remote computing verification problem, analysis of the existing research on security work, found that the existing calculation in advance and the second validation of remote computing method for cloud computing users spending too big, this paper combines the characteristics of cloud computing mass data processing, puts forward the remote computing verification method based on the third-party certification, efficient and low consumption to ensure security.

Cloud computing is the next stage in evolution of the internet, which provides large amount of computing and storage to customers provisioned as a service over the internet. However, cloud computing facing so many security challenges due to the possible compromise or byzantine failures. In this paper, we focus on Ensuring data storage security in cloud computing, which is an important aspect of Quality of Service (QoS). We propose an effective and flexible distribution verification protocol to address data storage security in cloud computing. In this protocol, we rely on erasure code for the availability, reliability of data and utilize token pre-computation using Sobol Sequence to verify the integrity of erasure coded data rather than Pseudorandom Data in existing system. Unlike prior works, our scheme provides more security to user data stored in cloud computing. The performance analysis shows that our scheme is more secure than existing system against Byzantine failure, unauthorized data modification attacks, and even cloud server colluding attacks.

The amount of Internet data is significantly increasing due to the development of network technology, inducing the appearance of big data. Experiments have shown that deep mining and analysis on large datasets would introduce great benefits. Although cloud computing supports data analysis in an outsourced and cost-effective way, it brings serious privacy issues when sending the original data to cloud servers. Meanwhile, the returned analysis result suffers from malicious inference attacks and also discloses user privacy. In this paper, to conquer the above privacy issues, we propose a general framework for Preserving Multiparty Data Privacy (PMDP for short) in cloud computing. The PMDP framework can protect numeric data computing and publishing with the assistance of untrusted cloud servers and achieve delegation of storage simultaneously. Our framework is built upon several cryptography primitives (e.g., secure multiparty computation) and differential privacy mechanism, which guarantees its security against semihonest participants without collusion. We further instantiate PMDP with specific algorithms and demonstrate its security, efficiency, and advantages by presenting security analysis and performance discussion. Moreover, we propose a security enhanced framework sPMDP to resist malicious inside participants and outside adversaries. We illustrate that both PMDP and sPMDP are reliable and scale well and thus are desirable for practical applications.

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.

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.

Ensuring data correctness and data completeness in cloud computing, in addition to security technologies, many other factors need also be considered, such as the behaviors of participants of cloud computing. This paper discusses the data correctness and completeness in cloud computing based on the perspective of game theory. It built a complete information static game theory frame between a trusted data center and an un-trusted database service provider, and gave the mixed strategy Nash equilibrium. It also proposed some countermeasures and suggestions to improve the data security in cloud computing.

Cloud computing is one of the most rapidly evolving technologies today; it provides numerous advantages that increase its affordability and dependability for use in the company. This paper goes over the concepts of cloud computing, such as its characteristics, deployment model, and service model, discusses the various benefits of cloud computing, and highlights the most pressing issues and security concerns in cloud storage. As a consequence, that leads to a review of distinctive cryptography algorithms that meet the security requirements (CIA: confidentiality, integrity, and availability) that are used to secure communications in cloud computing situations. It also displays many algorithms depending on the previous studies, such as Blowfish, RSA, DES, AES, MD5, Feistel, SP, HIGHT, LED, Cybpher, PRESENT, RC6, Diamond2, mCrypton, SLIM, Klein, PUFFIN-2, SEA, CLEFIA, LBlock, TWINE, 2, ANU, ANU-II, NLBSIT, Piccolo, BORON, RECTANGLE, LICI, QTL, LOGIC, TRIVIUM, Fruit-v2, Fruit-80, A4, the Enocoro family, and Grain family, to make a comparison among them using many measurements. It found that modern and lightweight algorithms are more suitable for use in this field. Furthermore, the purpose of this paper is to make some suggestions for improving the safety and security of cloud computing technologies.