Examining Cloud Computing's Data Security Problems and Solutions: Authentication and Access Control (AAC)

Today, virtualization is a key technology for cloud computing and modern data centers, providing scalability and security, managing the global IT infrastructure and reducing costs. Among the methods of virtualization, the most popular was containerization, that is the isolation of related groups of linux processes that share a common Linux kernel. Containerization is more profitable that classical hardware virtualization because of compactness of containers and lower overhead costs of memory, disk, CPU. However, in comparison with classical architectures without process isolation containers can cost more, and in any case, the industry is waiting for additional optimization – the speed of launch, saving memory and disk space and other resources. Different caching techniques can help in this, because Caching is the oldest mechanism of increasing software productivity without radical modification of algorithms and hardware. However, there are a lot of architectural and engineering tradeoffs. Here we will consider modern scientific and technical approaches to their solution in different aspects – acceleration of launch, optimization of shared usage, acceleration of building container images, as well as some security problems caused by aggressive caching in modern processor architectures. And in some use cases for multi-container systems performance and latency are not important, but we have to ensure the maximum load of physical servers. In these cases, the algorithms of planning and placement of containers are relevant, and we give an overview of theoretical work on this topic.

In the network era, the amount of data has surged, and a lot of data information has high density and high value information attributes, which also makes the problem of data security protection arouse the continuous attention of the society. Based on the purpose of data protection, people began to discuss the data security protection measures from the perspective of blockchain technology support. It is hoped that the development and application of blockchain technology can reduce the risk of damage in the process of data information exchange, accelerate the sharing of information data, and realize the marginalized processing of information ontology value, so as to maximize the stability of information transmission and ensure information security. This work first discussed the problem of computer data security and protection from the perspective of blockchain technology support, then clarified the specific measures of security protection and the application value of blockchain technology in computer data security, and finally provided certain theoretical research support for computer data security and protection.

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.

In Cloud Computing data security as well as load balancing of cloud server is very important and most challenging issues. Cloud reduces cost by providing on demand access of resources, it provides guaranteed, reliable services and dynamic allocation of resources. In cloud a single server stores very huge amount of data so there is need to balance load and provide fast transmission on cloud. Many users store their information on a single server, so there are chances of threats by unauthorized user. As we know on cloud we don’t have control over our data so security is a very important and challenging issue for cloud. If any algorithm provides fast transmission between user and server then there would be minimum chances for congestion and the data spend minimum time on transmission channel so it will give minimum time to attack on their data. In this paper, I proposed an ides and try to implement an algorithm through which cloud service provider can give the control to user itself. This algorithm uses a key and that key should be unknown from unauthorized person, the key may be user’s password or any secret information then this algorithm will give control to user and provide better security for user’s data where user would be free from authentication, correctness integrity or confidentiality. It is very important to .give control on user’s hand for reliability of services.

The considerable increase in the use of cloud computing and big data solutions requires the use of advanced technologies to ensure data security, privacy and dependability. One of the possible solutions is the use of data anonymization techniques, which performs an important role in data privacy protection. There are several techniques and algorithms to implement data anonymization. However, specifications and guidelines to guide and standardize the use of these resources are needed. These guidelines, called anonymization policies, are considered an important asset in organizations that want to protect their customer personal data. Although the use of data anonymization brings great benefits, there are no clear directions for data anonymization policies, which ends up lim- iting its adoption by companies and organizations. This work presents a generic anonymization policy to be used in cloud and big data platforms. It also presents a draft ontology with explicit formal specifications for data anonymization policies. This ontology has three main intentions: (i) to standardize the use of data anonymization policies; (ii) share common understanding of the structure of data anonymization among people; (iii) enable reuse of data anonymization policies. In this work, an ongoing study case within the EUBra-BIGSEA project will be implemented.

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.

Cloud computing and big data: Technologies and applications

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.

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.

Infrastructure as a Service (IaaS) provides logical separation between data, network, applications and machines from the physical constrains of real machines. IaaS is one of the basis of cloud virtualization. Recently, security issues are also gradually emerging with virtualization of cloud computing. Different security aspects of cloud virtualization will be explored in this research paper, security recognizing potential threats or attacks that exploit these vulnerabilities, and what security measures are used to alleviate such threats. In addition, a discussion of general security requirements and the existing security schemes is also provided. As shown in this paper, different components of virtualization environment are targets to various attacks that in turn leads to security issues compromising the whole cloud infrastructure. In this paper an overview of various cloud security aspects is also provided. Different attack scenarios of virtualization environments and security solutions to cater these attacks have been discussed in the paper. We then proceed to discuss API security concerns, data security, hijacking of user account and other security concerns. The aforementioned discussions can be used in the future to propose assessment criteria, which could be useful in analyzing the efficiency of security solutions of virtualization environment in the face of various virtual environment attacks.

In the quest for sustainable urban development, smart cities are increasingly harnessing cloud computing and big data analytics to optimize energy usage and drive policy initiatives. This review explores how leveraging these technologies can transform energy management within urban environments. Cloud computing provides the scalable infrastructure necessary for processing and storing vast amounts of data generated by smart sensors and IoT devices in real-time. By utilizing cloud-based platforms, cities can ensure the efficient aggregation, analysis, and dissemination of energy data, facilitating informed decision-making and policy formulation. Big data analytics plays a crucial role in this framework by enabling the analysis of complex and voluminous datasets to uncover patterns and insights that traditional methods may miss. Through advanced analytics, cities can gain a deeper understanding of energy consumption trends, predict peak demand periods, and identify opportunities for efficiency improvements. This data-driven approach allows policymakers to design and implement targeted energy policies, optimize resource allocation, and reduce operational costs. Key benefits of integrating cloud computing and big data analytics in energy optimization include enhanced real-time monitoring capabilities, improved accuracy in demand forecasting, and more effective management of energy resources. The ability to analyze and visualize large datasets supports the development of dynamic policies that adapt to changing conditions and promote energy efficiency across various sectors. Despite these advantages, challenges such as data privacy concerns, the need for robust cybersecurity measures, and the requirement for skilled personnel to interpret complex data must be addressed. Additionally, ensuring equitable access to these technologies across diverse urban areas is essential for maximizing their impact. In conclusion, the strategic application of cloud computing and big data analytics offers significant potential for optimizing energy use in smart cities. By embracing these technologies, urban centers can drive more effective and responsive energy policies, contributing to broader sustainability goals and enhancing overall quality of life.

<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>

ENERGY EFFICIENT ROBUST BACTERIAL FORAGING ROUTING PROTOCOL (EE-RBFRP) FOR BIG DATA NETWORK

Cloud computing is an on-demand resource sharing paradigm that serves users through Internet. The resource sharing extends from data to computing infrastructure resources. The term “cloud computing” generally refers to data centres with sufficient infrastructure accessed by various users over the Internet. Besides the benefits of cloud computing, data security is a significant concern and it is highly vulnerable to multiple attacks. Different applications and domains utilize cloud computing services to store their sensitive data in cloud servers. The specific data that were targeted by the attackers are the banking account information, organization information, etc. The sensitive information in cloud computing encompasses a wide range of domains, including personal and official information about an organisation, which, when breached, causes the entire system to fail. Numerous data security attacks occur worldwide. A few of the attacks that contributed to the system’s collapse is Yahoo’s, which has the dubious distinction of having experienced the largest major data breach in attack history, compromising over 3 million compromised accounts. This attracts the efforts of researchers and cybersecurity professionals to concentrate much on the security and privacy of sensitive data in cloud computing.

This paper presents a novel access control scheme for personal health record(PHR) data in cloud computing. The scheme utilizes attribute-based encryption(ABE), hash function and symmetric encryption to realize a fine-grained, multi- privilege access control to PHR. The patients can share their PHR with medical staff from various departments with different privileges securely. The experimental results show the efficiency of our scheme in terms of running-time, communication cost and storage overhead.