Role Based Secure Data Access Control for Cost Optimized Cloud Storage Using Data Fragmentation While Maintaining Data Confidentiality

The paper proposes a role-based secure data access control framework for cost-optimized cloud storage, addressing the challenge of maintaining data security, privacy, integrity, and availability at lower cost. The proposed framework incorporates a secure authenticity scheme to protect data during storage or transfer over the cloud. The framework leverages storage cost optimization by compressing high-resolution images and fragmenting them into multiple encrypted chunks using the owner's private key. The proposed approach offers two layers of security, ensuring that only authorized users can decrypt and reconstruct data into its original format. The implementation results depicts that the proposed scheme outperforms existing systems in various aspects, making it a reliable solution for cloud service providers to enhance data security while reducing storage costs

Evaluation of Cloud-Based Cyber Security System

Cloud-based cyber security systems leverage the power of cloud computing to protect digital assets from cyber threats. By utilizing remote servers and advanced algorithms, these systems provide real-time monitoring, threat detection, and incident response. They offer scalable solutions, enabling businesses to adapt to evolving threats and handle increasing data volumes. Cloud-based security systems provide benefits such as reduced infrastructure costs, continuous updates and patches, centralized management, and global threat intelligence. They protect against various attacks, including malware, phishing, DDoS, and unauthorized access. With their flexibility, reliability, and ease of deployment, cloud-based cyber security systems are becoming essential for organizations seeking robust protection in today's interconnected digital landscape. The research significance of cloud-based cyber security systems lies in their ability to address the growing complexity and scale of cyber threats in today's digital landscape. By leveraging cloud computing, these systems offer several key advantages for researchers and organizations: Scalability: Cloud-based systems can scale resources on-demand, allowing researchers to handle large volumes of data and analyze complex threat patterns effectively. Cost-efficiency: The cloud eliminates the need for extensive on-premises infrastructure, reducing costs associated with hardware, maintenance, and upgrades. Researchers can allocate resources based on their needs, optimizing cost-effectiveness. Real-time monitoring and threat detection: Cloud-based systems provide real-time monitoring of network traffic, enabling quick identification of suspicious activities and potential threats. Researchers can leverage advanced analytics and machine learning algorithms to enhance threat detection capabilities. Collaboration and knowledge sharing: Cloud platforms facilitate collaboration among researchers and organizations by enabling the sharing of threat intelligence, best practices, and research findings. Compliance and regulatory requirements: Cloud platforms often offer built-in compliance features and tools to meet regulatory requirements, assisting researchers in adhering to data protection and privacy standards. Overall, the research significance of cloud-based cyber security systems lies in their ability to provide scalable, cost-effective, and advanced security capabilities, empowering researchers to mitigate evolving cyber threats and protect sensitive data and systems effectively. We will be using Weighted Product Methodology (WPM) which is a decision-making technique that assigns weights to various criteria and ranks alternatives based on their weighted scores. It involves multiplying the ratings of each criterion by their corresponding weights and summing them up to determine the overall score. This method helps prioritize options and make informed decisions in complex situations. Taken of Operational, Technological, Organizational Recorded Electronic Delivery, Recorded Electronic Deliver, Blockchain technology, Database security, Software updates, Antivirus and antimalware The Organizational cyber security measures comes in last place, while Technological cyber security measures is ranked top and Operational measures comes in between the above two in second place. In conclusion, a cloud-based cyber security system revolutionizes the way organizations safeguard their digital assets. By utilizing remote servers, advanced algorithms, and real-time monitoring, it offers scalable and robust protection against evolving threats. With features like threat detection, data encryption, and centralized management, it ensures enhanced security, agility, and efficiency. Embracing a cloud-based approach empowers organizations to stay ahead in the ever-changing landscape of cyber security, effectively safeguarding their critical data and infrastructure

A Secure Cloud Computing Model based on Data Classification

AbstractIn cloud computing systems, the data is stored on remote servers accessed through the internet. The increasing volume of personal and vital data, brings up more focus on storing the data securely. Data can include financial transactions, important documents, and multimedia contents. Implementing cloud computing services may reduce local storage reliance in addition to reducing operational and maintenance costs. However, users still have major security and privacy concerns about their outsourced data because of possible unauthorized access within the service providers. The existing solutions encrypt all data using the same key size without taking into consideration the confidentiality level of data which in turn will increase the cost and processing time. In this research, we propose a secure cloud computing model based on data classification. The proposed cloud model minimizes the overhead and processing time needed to secure data through using different security mechanisms with variable key sizes to provide the appropriate confidentiality level required for the data. The proposed model was tested with different encryption algorithms, and the simulation results showed the reliability and efficiency of the proposed framework

Cloud data security and various cryptographic algorithms

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

A Secured Cloud Data Storage with Access Privileges

In proposed framework client source information reinforcements off-site to outsider distributed storage benefits to decrease information administration costs. In any case, client must get protection ensure for the outsourced information, which is currently safeguarded by outsiders. To accomplish such security objectives, FADE is based upon an arrangement of cryptographic key operations that are self-kept up by a majority of key supervisors that are free of outsider mists. In unmistakable, FADE goes about as an overlay framework that works flawlessly on today's distributed storage administrations. Actualize a proof-of-idea model of FADE on Amazon S3, one of today's distributed storage administrations. My work oversee, esteem included security highlights acclimatize were today's distributed storage administration. our research work proceeds in ensuring the file access control and assured deletion in multi cloud environment and reducing the meta data management, there by the cloud storage become more attractive and many users will adopt the cloud space in order to diminish the data storage cost

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Hybrid Simulation and Test of Vessel Traffic Systems on the Cloud

This paper presents a cloud-based hybrid simulation platform to test large-scale distributed System-of-Systems (SoS) for the management and control of maritime traffic, the so-called Vessel Traffic Systems (VTS). A VTS consists of multiple, heterogeneous, distributed and interoperating systems, including radar, automatic identification systems, direction finders, electro-optical sensors, gateways to external VTSs, information systems; identifying, representing and analyzing interactions is a challenge to the evaluation of the real risks for safety and security of the marine environment. The need for reproducing in fabric the system behaviors that could occur in situ demands for the ability of integrating emulated and simulated environments to cope with the different testability requirements of involved systems and to keep testing cost sustainable. The platform exploits hybrid simulation and virtualization technologies, and it is deployable on a private cloud, reducing the cost of setting up realistic and effective testing scenarios