SDSPF: A Secure Data Storage and Processing Framework for Cloud Computing Systems

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

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 proliferation of the Internet, IoT, and Cloud Computing, there is now an abundance of virtual data in every industry, field of study, and government agency. Big data has quickly become a topic of intense interest, garnering media coverage and commentary from all over the world. Data privacy and security in Big Data is a pressing concern. The 5Vs of big data—size, velocity, value, veracity, and variety—lower the bar for adequate protection. This paper aimed to draw attention to security and privacy issues and challenges associated with Big Data in healthcare, the resolution of which could result in a more secure data processing and computing infrastructure. This paper also provides a high-level overview of the K-Anonymity technique for protecting the privacy of large datasets before they are released for analysis, with the goal of preventing the disclosure of personally identifiable information. In conclusion, this paper summarizes the functions and features of the best big data security solutions offered by industry leaders.

Homomorphic cryptosystem-based secure data processing model for edge-assisted IoT healthcare systems

With the increasing popularity of network technology and information concepts, Big Data (BD) and cloud computing technologies have emerged and are widely used in all industries. BD technology can fully utilize the application value of information. Cloud computing can store a large amount of data, and improve data usage efficiency, so as to make full use of the value of data. At the same time, data security issues have become increasingly important. However, due to various factors, many users are faced with data breaches and privacy violations in the BD cloud computing environment. In this case, there are some data security risks, and the key issue is to consider how to avoid these risks. By analyzing the relationship and differences between BD and cloud computing, this article studied the issues and influencing factors of Artificial Intelligence (AI) data security in the BD cloud computing environment, and proposed corresponding optimization strategies, so as to improve data security and provide users with a brand new experience. Through comparison, it could be seen that the data processing speed and legal integrity after using the optimization strategy significantly improved. Among them, data processing speed increased by 7.2% and legal integrity increased by 10.4%. BD cloud computing could effectively improve AI data security performance.

Regulatory authorities worldwide are developing cybersecurity standards and data protection regulations related to Internet of Things (IoT) devices to protect user information. As smart IoT technologies evolve, the implementation of such regulations and standards is highly crucial for allowing the secure storage, processing, and transfer of data. Given the ongoing research and development of smart IoT technologies across various domains, we aim to investigate whether critical compliance standards are adequately addressed in existing academic research. Through an in-depth analysis utilizing topic modeling and large language model (LLM) approaches, we identified the top five researched areas and their relationship with relevant regulations. We identified that while substantial progress has been made in addressing the technical challenges associated with IoT privacy, there remains an urgent need for research that bridges these solutions with socio-technical systems and compliance. Our research emphasizes the importance of adopting privacy-by-design at the initial stages of IoT implementation. Additionally, we determined research gaps in existing studies and proposed future research directions emphasizing the development of IoT frameworks that are not only technologically robust but also align with evolving legal compliance standards.

Despite the promising application of cloud computing within government organisations, determining the barriers to the successful implementation of cloud computing remains a major challenge. Often, government organisations face formidable barriers to fully adopting cloud computing functions. These barriers include organisational culture issues, a weak application of service-level agreements (SLAs), compliance issues, interoperability and integration issues, the lack of an organisational strategy, cost calculation methods, cloud service availability, and cloud data security and privacy, as well as insufficient trust in providers. Tackling these barriers while meeting the specifications of government cloud computing involves architectural requirements and mechanisms. In addition, the nature of cloud ecosystem operation and its alignment with appropriate strategies are known to be complex, also considering the growing desire for governance solutions. Although researchers and organisations have recognised cloud computing obstacles prior to the cloud implementation stage, fewer efforts have been made to investigate cloud barriers in-depth during and after cloud implementation projects or when introducing governance solutions. This might partly be attributable to inadequate enterprise requirements for addressing cloud barriers, the complexity of government cloud specifications, and the lack of involvement by cloud stakeholders, such as providers and consumers. Thus, the aim of this dissertation is to develop a governance method consisting of the generation and testing of architectural requirements to overcome dominant cloud barriers and improve cloud computing functionalities in the implementation stage. To achieve this, the roles of related cloud providers and consumers have been investigated. The study was guided by activities of The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM) to design related architectural requirements that would provide best practices for the development of the governance method. Using a sequential, exploratory mixed method, multiple qualitative case studies on the role of cloud providers were conducted to explore which and how architectural requirements are suitable and applicable for addressing the identified cloud barriers. Afterwards, a quantitative survey study was performed to examine and validate the findings from the qualitative phase and to discover the role of cloud consumers in tackling cloud barriers. The utilisation of dual perspectives, from cloud providers and consumers, to design and measure the suitability and applicability of the architectural requirements is a substantial theoretical contribution in the field of enterprise information systems and cloud governance. Both cloud providers and consumers in the selected research context balanced their roles in the composition of cloud ecosystem projects, reducing complexity and enabling cooperative solutions for handling cloud barriers with different levels of emphasis and prioritisation. The results of this thesis provide significant guidance in eliciting architectural requirements as well as in their successful contribution to addressing the 10 identified cloud barriers in the selected context. Seeking a cloud provider’s perspective through multiple case studies resulted in 24 confirmed, newly emerged architectural requirements and 48 sub-requirements for best practices to support Information Technology (IT) managers and practitioners during the implementation of government cloud projects. From the point of view of the cloud service consumer, statistical evidence showed that the architectural requirements proved to be relevant and applicable and resulted in the validation of only eight important cloud constructs with a scale of 36 architectural requirements. The results at the governance level also produced a clear method that can enhance the realisation of value when business/IT strategies and EA governance practices both take place in cloud projects. The governance method also consists of a set of architectural requirements to support managing and controlling cloud computing implementation projects with related stakeholders and change management initiatives. The architectural requirements have been modelled via the ArchiMate 3.0 modelling language as unique and beneficial contributions for the enhancement of enterprise architecture practices. This thesis thereby introduces a novel cloud governance method with clear mechanisms and practices to serve as crucial resources for related organisations, decision-makers, business practitioners and researchers.

ABSTRACT: As cloud computing continues to play a pivotal role in data storage and processing, ensuring the security and integrity of transferred data remains a critical concern. This project introduces a novel approach for secure data transfer and detection leveraging the Counting Bloom Filter in cloud computing environments. The proposed system addresses the vulnerabilities associated with traditional data transfer methods by incorporating the Counting Bloom Filter, a probabilistic data structure. This technology aids in optimizing storage efficiency and enhances the accuracy of data detection, crucial for maintaining data integrity during transmission. The project focuses on these cure transfer of sensitive information within cloud computing environments, providing an added layer of protection against unauthorized access and potential data corruption. The Counting Bloom Filter is employed to detect anomalies and ensure the integrity of the transferred data, contributing to a robust and reliable cloud-based data transfer mechanism. Key objectives of the project include the implementation of advanced encryption techniques for secure data transfer, the integration of the Counting Bloom Filter for efficient data detection, and the development of a comprehensive system ensuring data integrity in the cloud. The project's innovation lies in its ability to enhance the security and reliability of data transfer in cloud computing, addressing the evolving challenges associated with data protection and privacy. In conclusion, the Secure Data Transfer and Detection from Counting Bloom Filter in Cloud Computing project represents a significant advancement in the realm of secure data transmission. By combining encryption methods with the unique capabilities of the Counting Bloom Filter, the project offers an effective solution for securing data during transfer, ensuring the integrity of information in cloud computing environments. Keywords:- Cloud computing, Data storage, Data processing, Data transfer, Security, Integrity, Counting Bloom Filter, Probabilistic, data structure, Storage efficiency, Data detection, Sensitive information, Encryption techniques, Unauthorized access, Data corruption, Anomaly detection, Data protection, Privacy, Secure data transfer, Cloud-based data transfer, System development, Data transmission.

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. User’s data is stored in large database. The stored data can be accessed and modified by the clients over the Internet. The data is monitored by the Third Party Auditor (TPA) on behalf of the client. Therefore, integrity is lacked by the data stored on the servers. The data integrity is ensured by the cloud services that provide trust to the privacy of users. Aiming at the urgent problem of network data security in cloud computing operations, this paper proposed a security situation assessment system to grasp the security situation in real time. A set of cloud computing network data storage security is proposed. Through this model, the extraction of network data storage security situation elements, the design of network data storage security situation assessment scheme and the calculation method of network data storage security situation value are completed. The experimental results show the error of the predicted value obtained by the network data storage security situation assessment system. The effectiveness of the system model and the superiority of the improved algorithm is verified by the experimental results. This paper uses the cloud model to predict the cloud computing network security situation. On the other hand, the security situation value obtained by the situation assessment process can be used directly without training the original situation value. Performance improvement by the proposed technique over existing technique is seen and it is observe that the proposed technique is 23% and 34% better than the existing techniques.

The Rapid expansion of cloud computing with data-centric services makes the protection of sensitive information via confidentiality, integrity, and availability indeed one of the most critical challenges. This work introduces a unified framework that integrates the advanced encryption standard (AES) with end-to-end encryption (E2EE) to secure data processing and transmission in a cloud environment. The proposal addresses security vulnerabilities that arise from multi-tenancy, third-party management, and remote access by encrypting data at the source and keeping its confidentiality throughout its lifecycle while it exists in the cloud. By using AES-CBC mode and a robust key-management scheme, the system guarantees secure data transmission, storage, and access control. Experimental evaluation indicates that the integrated AES-E2EE model reduces latency in comparison to traditional HTTPS (230 MS) and standalone AES (200 MS) significantly reaching dimensions as small as 175 MS, paving way for use in latency-sensitive applications. The results validate scaling, efficiency, and protection from present cyber threats of the proposed framework development towards building secure and trustworthy cloud computing infrastructures.

Cloud computing is completely about productivity, economy, and corporate agility. But there raises a question how we can commercially attain those benefits if their cloud management solution becomes inefficient, incomplete, and inflexible. Even though there are many aspects in cloud environment. The data security, confidentiality and privacy plays a major role in cloud deployment model. In case of private cloud the identity anonymization and secured data storage becomes essential to address. But we as developers of cloud can maintain the cloud with perfect methods and protocols which make the cloud service delivery more efficient, productive, and agile. In this paper a method for identity anonymization and secure data storage in private cloud using GDS (Group Digital Signature) is proposed and implemented.

In order to protect the security and privacy of big data, the cloud storage service needs to enforce effective access control mechanism on user requests. Attribute-Based Encryption is a promising cryptographic access control technique to ensure the end-to-end security of data in cloud. However, the existing ABE researches mainly focus on the efficiency decryption, while the flexibility of policy, the communication cost, and the metadata management of ciphertexts are still challenging issues in the big data environment. In this paper, for the first time, we propose a new distributed, scalable and fine-grained access control scheme based on classification attributes for the cloud object storage. The classification attributes and threshold policies are integrated into an access structure, and then the objects are encrypted with the integrated access structure. The constant-size ciphertext components related to attributes can be managed as the corresponding metadata. As a result the encryption complexity and ciphertext storage are reduced. In addition, we present a new label-based access control model with multi-authorities to describe the detailed relationships of entities in our scheme. Besides, the proposed scheme is proved to be secure under l-BDHE assumption, and the system implementation demonstrates the practical feasibility and good performance.

We describe a novel approach for building a secure and fault tolerant data storage service in collaborative work environments, which uses perfect secret sharing schemes to store data. Perfect secret sharing schemes have found little use in managing generic data because of the high computation overheads incurred by such schemes. Our proposed approach uses a novel combination of XOR secret sharing and replication mechanisms, which drastically reduce the computation overheads and achieve speeds comparable to standard encryption schemes. The combination of secret sharing and replication manifests itself as an architectural framework, which has the attractive property that its dimension can be varied to exploit tradeoffs amongst different performance metrics. We evaluate the properties and performance of the proposed framework and show that the combination of perfect secret sharing and replication can be used to build efficient fault-tolerant and secure distributed data storage systems.

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.

Cloud computing is the most demanded advanced technology throughout the world. It is one of the most significant topic whose application is being researched in today's time. One of the prominent services offered in cloud computing is the cloud storage. With the cloud storage, data is stored on multiple third party servers, rather than on the dedicated server used in traditional networked data storage. All data stored on multiple third party servers is not cared by the user and no one knows where exactly data saved. It is cared by the cloud storage provider that claims that they can protect the data but no one believes them. Data stored over cloud and flow through network in the plain text format is security threat. This paper proposes a method that allows user to store and access the data securely from the cloud storage. It also guarantees that no one except the authenticated user can access the data neither the cloud storage provider. This method ensures the security and privacy of data stored on cloud. A further advantage of this method is that if there is security breach at the cloud provider, the user's data will continue to be secure since all data is encrypted. Users also need not to worry about cloud providers gaining access to their data illegally.