Cloud Storage Research Articles

An improved finegrained ciphertext policy based temporary keyword search on encrypted data for secure cloud storage

We present a temporary keyword search over sensitive and confidential health data in a cloud environment. The cloud constitutes a semi-trusted domain, making it necessary for data owners to secure their data before outsourcing it through techniques like encryption. Attribute-based keyword search techniques tend to perform a search operation using a search token generated by an authorized user. These search tokens can lead to serious privacy threats, as they can extract all ciphertexts that may have been generated along with their keyword. Therefore, restricting search tokens to extract ciphertexts generated within a time interval is a more promising solution. In this paper, we present a novel ciphertext policy fine-grained temporary keyword that prevents the misuse of these search tokens. Further, it mitigates the risk of insider threats within healthcare organizations by limiting the window of opportunity for unauthorized access to minimum. To assess the security, our proposed scheme is formally proven to be secure against Selectively Chosen Keyword Attacks in the generic bilinear group model. Additionally, we demonstrate that the encryption algorithm’s complexity is linear in relation to the number of attributes. Our scheme’s significance and practicality are revealed by the performance evaluation.

End-to-End Encrypted Cloud Storage

End-to-End Encrypted Cloud Storage

"Cloud Storage of Data and the Crime of Storage of “Electromagnetic Records” under Criminal Law in Japan"

"Cloud Storage of Data and the Crime of Storage of “Electromagnetic Records” under Criminal Law in Japan"

Performance enhancement of cloud security with migration algorithm for choosing virtual machines in cloud computing

The management of the hardware as well as software resources entrusted to third-party service suppliers have shown to be extraordinarily remarkable and accessible with Cloud Computing (CC), a potential computing technology for the future. Yet, a major undertaking is characterized by security as well as the use of Virtual Machines (VMs) to perform a request. The main issue with using technology is security worries, which raises the need for a strong security system to safeguard data on the cloud. Thus, to address this problem, a method called Weighted Mean-based Radial Basis Function Network with Advanced Encryption Standard (WMRBFN-AES) is developed to improve cloud security along with the time of execution. Random keys are produced utilizing the Pseudo Random Number Generator (PRNG) technique, and the best keys are created employing the WMRBFN technique. In the WMRBFN technique, randomly generated keys are used as input, and the hidden layers yield an estimate associated with the secret key of the input. The output layer generates the most precise secret keys to encrypt the data. Encryption is carried out using the AES algorithm. The files about the encrypted data are kept in a cloud storage system. Reduced execution times for stakeholder demands (tasks) as well as optimal cloud resource utilization result from the selection of VMs, which significantly improves reliability. The Migration Algorithm (MA) is used in the cloud framework to optimize the selection of VMs to improve resource utilization and performance. The experimental findings of the suggested framework and the state-of-the-art frameworks are compared, and it was found that the new model performed better in terms of cost, resource utilization, service latency, throughput, and execution time.

Cryptography in cloud: An in-depth investigation into encryption mechanisms for safeguarding cloud-based data

Cloud computing is one of the most significant computing models, which provides easy access of resources. It is an alternative for costlier data framework and networking models. The services are provided to the data users based on their requirements. More industries are depending on the cloud, because of its fast computations and robust nature. However, there are several security concerns and threats in cloud storages. Moreover, large amounts of data are shared between people and organizations through the cloud. The data owner doesn’t have control over the data in the cloud. To address the security and privacy issues in the cloud, this thesis aims at overcoming this trade-off, while considering security.

Research on Speech Communication Enhancement of English Web-based Learning Platform based on Human-computer Intelligent Interaction

This study presents a novel web-based learning platform that leverages human-computer intelligent interaction to enhance English communication skills. The platform integrates cutting-edge technologies to create an immersive learning experience, combining natural language processing, speech recognition, and interactive exercises. Learners engage in real-time conversations with virtual tutors, receive personalized feedback, and access a vast repository of educational resources. The platform not only facilitates language acquisition but also encourages self-paced learning, making it a valuable tool for both educators and students. By harnessing the power of artificial intelligence, this web-based platform represents a significant advancement in the realm of English language education. To overcome these issues this paper proposed SVM with an improved satin Bower bird optimization algorithm (SVM-ISBBO). SVM-ISBBO uses fog computing services that minimize the latency and speeds up the process, effectively handling huge wearable devices. In this proposed work SVM-ISBBO monitors the students communication, vocal parameters, blood pressure, etc, and these values are obtained from wearable sensor devices and their notifications are sent back to teachers. Teachers diagnosed the student information and sent back the alert notifications to the students for taking proper medications. All this information is stored in fog-based cloud storage in a secure manner. The accuracy rate of KNN got 78.56%, NB got 81.74%, SVM got 85.15% and the proposed work of SVM-ISBBO got 92.34%.

Integrating IoT with WSNs using fuzzy logic-based middleware

With the rapid adoption of cloud storage for business and personal use, data security has become a significant concern. This study investigates the effectiveness of advanced encryption algorithms to ensure the integrity, confidentiality, and availability of data stored in cloud environments. The research focuses on the implementation and evaluation of three encryption algorithms: AES-256, ChaCha20, and Threefish, comparing their performance in terms of computational complexity, key generation, and resistance to various attacks. The study utilizes a testbed consisting of a simulated cloud storage environment, where the encryption algorithms are deployed and assessed based on encryption/decryption time and throughput. Results indicate that the ChaCha20 algorithm outperforms both AES-256 and Threefish in terms of encryption/decryption speed while maintaining strong security. Moreover, the findings suggest that the combination of these encryption algorithms can enhance data security by providing a multi-layered defense mechanism against potential threats. The research contributes to the advancement of cloud storage security by identifying optimal encryption algorithms and proposing a robust solution for safeguarding sensitive information.

MapReduce based on serverless platforms

This study intends to investigate the application of the MapReduce (MR) framework based on serverless computing in big data processing. By combining the MapReduce model with serverless computing, efficient data processing is achieved. In this framework, the phases of Map task execution, reduce task execution, etc. are accomplished through stateless serverless functions, and data storage is realized with the help of cloud storage platforms (e.g., OSS). In this paper, the author introduces the basic theory of MR, the basic theory of serverless computing, describes the framework implementation process, and discusses the role of OSS in distributed computing. The outcomes of the trial indicate the average execution time of the framework for a WordCount task on 100 pieces of TOEFL English reading data is 6.81 seconds. The discussion analyzes the frameworks advantages (high elasticity, resource utilization) and disadvantages (cold start latency, unsuitable for long time tasks). Future research directions encompass performance optimization, long-time task processing, state management, etc. In summary, this study provides valuable insights for the practical application of serverless computing in big data processing.

Reliable edge-to-core optical networks: An optimal algorithm for maximal path diversity

With the emergence of IoT applications, 5G, and edge computing, network resource allocation has shifted toward the edge, bringing services closer to the end users. These applications often require communication with the core network for purposes that include cloud storage, compute offloading, 5G-and-Beyond transport communication between centralized unit (CU), distributed unit (DU) and core network, centralized network monitoring and management, etc. As the number of these services increases, efficient and reliable connectivity between the edge and core networks is of the essence. Wavelength Division Multiplexing (WDM) is a well-suited technology for transferring large amounts of data by simultaneously transmitting several wavelength-multiplexed data streams over each single fiber optics link. WDM is the technology of choice in mid-haul and long-haul transmission networks, including edge-to-core networks, to offer increased transport capacity.Optical networks are prone to failures of components such as network fiber links, sites, and transmission ports. A single network element failure alone can cause significant traffic loss due to the disruption of many active data flows. Thus, fault-tolerant and reliable network designs remain a priority. The architecture called “dual-hub and dual-spoke” is often used in metro area networks (MANs). A dual-hub, or in general a multi-hub network, consists of a set of designated destination nodes (hubs) in which the data traffic from all other nodes (the peripherals) should be directed to the hubs. Multiple hubs offer redundant connectivity to and from the core or wide area network (WAN) through geographical diversity. The routing of the connections (also known as lightpaths) between the peripheral node and the hubs has to be carefully computed to maximize path diversity across the edge-to-core network. This means that whenever possible the established redundant lightpaths must not contain a common Shared Risk Link Group (SRLG).An algorithm is proposed to compute the most reliable set of SRLG disjoint shortest paths from any peripheral to all hubs. The proposed algorithm can also be used to evaluate the overall edge-to-core network reliability quantified through a newly introduced figure of merit.

An Introduction to Computational Science and Computer Development

As our economy and society continue to evolve, computer technology is also evolving and innovating. The emergence of computer technology has greatly facilitated human work and learning, and has brought great influence to all walks of life. For a long time, computer technology has deeply penetrated into people's production and life, and become an indispensable and important part. To a certain extent, computers have contributed to the development of the national economy, and at the same time, it has also brought a lot of convenience to the people. This initiative not only significantly improves the quality of life and productivity of the general public, but also an important indicator of the overall quality of a country's population. Through the study, it is found that with the continuous development of science and technology, the trend of combining computer technology and communication technology is becoming more and more obvious and has made certain achievements. Although the development of computer science and technology is very short, but the scope of its application for people's lives and the reform and development of related work is unparalleled by any other technology. The application of computer science and technology basically involves all aspects of domestic life, from the most basic food, clothing, housing and transportation to human communication and all kinds of production work, at the same time, computer science and technology is also the core of other technological reforms and innovations, and other technological advances relying on computer science and technology, cloud computing technology, cloud storage and its intelligent regulation and control of the new technology have achieved a certain degree of fundamental independent innovation. This research will focus on the computer science and technology, which is the core of other technological reforms and innovations. This study will systematically analyze the practical application and development of computer science and technology, and carefully sort out the main reasons for the development of computer science and technology and the future development of the market outlook.

ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring.

Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing's proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.

A Novel Design of a Low-Cost SCADA System for Monitoring Standalone Photovoltaic Systems

Standalone photovoltaic (PV) systems are pivotal in the global transition towards sustainable energy, offering reductions in fossil fuel dependence and helping homes and businesses lower electricity costs. Key to optimizing the performance and efficiency of standalone systems are supervisory control and data logging (SCADA) systems. They monitor and record operational data such as power output, facilitating early detection of potential issues. This paper introduced a novel design for both the Human-Machine Interface (HMI) and data storage in a SCADA system for standalone PV systems, addressing two crucial aspects: real-time monitoring and efficient data retrieval, both at very low cost. The proposed design utilized Bluetooth Low Energy technology to transmit voltage and current data from the PV panel to a mobile application, marking a departure from traditional HMI approaches. This method enabled historical data analysis for trend identification. Additionally, the system intermittently transferred collected data to a cost-effective cloud storage service via Wi-Fi, allowing for substantial data storage at no cost. Remote data storage, another key feature of this design, simplifies data retrieval, which is particularly beneficial for systems in rural areas. Emphasizing open-source development, this design ensured flexibility and customization options. To demonstrate its practical effectiveness of the design, a one-day power curve of the PV system and the battery voltage data are presented, showcasing the design's capability in handling extensive and remote data storage.

A survey for managing temporal data in RDF

The Internet serves not only as a platform for communication, transactions, and cloud storage, but also as a vast knowledge store where both people and machines can create, manipulate, infer, and utilize data and knowledge. The Semantic Web was developed to facilitate this purpose, enabling machines to understand the meaning of data and knowledge for use in decision-making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web, which is organized into layers known as the Semantic Web Layer Cake. However, RDF’s basic construct is a binary relationship in the format of <subjectpredicateobject>. Representing higher-order relationships with RDF requires reification, which can be cumbersome. Time-varying data is prevalent, but cannot be adequately represented using only binary relationships. We conducted a detailed review of the literature on extending RDF with temporal data, comparing approaches for representation, querying, storage, implementation, and evaluation. In addition, we briefly reviewed approaches for extending RDF with spatial, probability, and other dimensions in conjunction with temporal data.

Enhanced Privacy for Health Records Using Blockchain Technologies

This project deals with the security of digital medical history by implementing advanced measures such as encryption using blockchain methods, access control. Thanks to the integration of encryption, blockchain, dynamic access control and security even after decryption, a robust privacy framework is built to meet evolving cyber threats and strengthen security and availability of health data. This conventional method of storage deprives doctors and researchers of capitalizing on modern technologies and healthcare engines to perform clinical tests, improve patient care, and contribute to future clinical research [1]. By empowering patients using blockchain technology, this project is transforming healthcare data management to improve patient care and promote the development of a reliable healthcare environment, which means remarkable progress towards improving Privacy for Health Records Using Blockchain Technologies. Key Words: Cloud Storage Security, Blockchain Technology, Health Records, Privacy and Security, Blockchain Algorithms, Keccak, Keras, Tesseract

Secure medical data on cloud storage via DNA homomorphic encryption technique

BackgroundCloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. MethodsHowever, an effective encryption scheme is needed to protect the private medical data of the user in the cloud without leaking it. Therefore, in this paper an efficient Deoxyribonucleic Acid Homomorphic Encryption algorithm (DNA-HE) has been proposed to encrypt and store the medical images in the cloud securely. Initially, the data is mapped to DNA sequences and then encrypted using homomorphic encryption. The key for the homomorphic algorithm is generated using Rider Optimization Technique which ensures security. It acts as a double encryption technique. The same procedure tends to decrypt the encrypted data. ResultsThe efficiency of the proposed technique is assessed employing several parameters such as execution time, encryption time, and decryption time. The experimental results shows that the proposed DNA-HE system reduces encryption time of 10 %, 20 %, and 35 % than OHE, HE and ECC algorithms.

Efficient secure channel free identity-based searchable encryption schemes with privacy preserving for cloud storage service

Cloud storage is a basic service model of the cloud computing. Since the cloud data is stored in the ciphertext for the data confidentiality, how to search the cloud data again in ciphertext state for users is a natural requirement. The searchable encryption (SE) gives an efficient solution to achieve both the data confidentiality and the searchable requirement simultaneously. In SE schemes, the user can transfer a search trapdoor to the cloud server from a secure channel and the cloud server can use the received trapdoor to search the corresponding keyword ciphertext. Since the keywords are with low entropy, the traditional SE is vulnerable to keyword guessing attack. In order to remove the need of the secure channel and also to achieve the security against the keyword guessing attack, we build a single-user identity-based secure channel free SE scheme in the random oracle model by using lattice-based tools. Only the designated cloud server can finish the ciphertext search which means the secure channel between the user and the cloud server is not needed in the proposed scheme. Under the hardness of the learning with errors problem, we prove that the proposed scheme is ciphertext indistinguishable under the selective identity and chosen keyword attacks. Moreover, the proposed scheme also satisfies the trapdoor indistinguishability which directly leads to the security against the outside offline keyword guessing attack. Furthermore, we extend the proposed single-user scheme into the multi-user scenario. The proposed multi-user scheme inherits all functions achieved in the single-user scheme, such as secure channel free. Meanwhile, ciphertext length is fixed with the number of users increasing, which is more suitable for the large group users. In addition, we analyze the theoretical evaluation and experimental evaluation between the proposed schemes and some known literatures respectively. The results demonstrate that our proposed algorithms have several advantages in the both single-user and multi-user schemes, for example, the ciphertext lengths in the single/multi-user schemes are only 5.632KB and 6.016KB respectively and the running time of Test algorithm for any user groups is fixed to be 0.65ms in our simulated experiments.

Machine learning-enhanced hybrid cryptography and image steganography algorithm for securing cloud data

Ensuring data security in cloud computing is crucial due to the growing reliance on cloud-based services. Hybrid cryptography and image steganography have emerged as robust techniques to enhance data confidentiality in the cloud. In this research paper, we propose a novel algorithm, “Machine Learning-Enhanced Hybrid Cryptography and Image Steganography,” integrating these methods to provide comprehensive data protection. The algorithm employs key generation, encryption, steganography, cloud storage, data retrieval, and machine learning-based attack detection to defend against advanced cyber threats. Our experimentation demonstrates the algorithm’s effectiveness in detecting DoS attacks, data breaches, and data leakage attempts using SVM, Neural Network, Isolation Forest, and Random Forest models. The proposed approach offers broad applicability, fortifying data security and fostering further advancements in cloud security research.

Identity-based remote data integrity auditing from lattices for secure cloud storage

Identity-based remote data integrity auditing from lattices for secure cloud storage

Cloud Security System for ECG Transmission and Monitoring Based on Chaotic Logistic Maps

Biomedical data or information must be transmitted securely via the internet for smart healthcare. The Electrocardiogram (ECG) signal is amongst the most essential clinical signals which must be delivered to hospital facilities. Prime focus of this research is on the encryption of ECG for secure transmission. Chaos theory is used for the development of deterministic nonlinear systems, that can be used to create random numbers for the Chaotic Logistic Map (CLM) based encryption. This study describes a cryptographic algorithm for encrypting ECG signals that uses a mix of the CLM and fingerprint data. The common factor between the patient section and monitoring section is the operation on sample data points of ECG. The choice of proper encryption and decryption theme can save more amount of time and is invulnerable both to noise-based attacks and hacking instances. The proposed framework is implemented on Dropbox based cloud storage and access is possible from any given locations. Simulation tests are used to assess the system performance in terms of Structural Similarity Index Matrix (SSIM), Histogram, Spectral Distortion (SD), Correlation and Log-Likelihood Ratio (LLR). The incorporation of complex layers of CLM encryption increases security.

A comprehensive investigation of researchers’ shared file management practices in cloud storage

ABSTRACT The use of cloud storage, which provides ubiquitous and simultaneous access to files, is prevalent among researchers for their collaborative projects. This study comprehensively examined researchers’ shared file management practices in cloud storage by conducting an online survey with 534 researchers across disciplines. This study uncovered activities that needed better support and possible ways of making shared file management practices more satisfactory by identifying variables associated with satisfaction. The findings revealed different practices and showed that researchers were not active in managing shared files. Among different shared file management activities, finding/re-finding, archiving, and organizing activities needed better support. Researchers’ satisfaction was the highest when their research team had explicit rules for organizing and archiving shared files, when they organized files regularly, used location/organization of the files when finding/re-finding files, and managed shared files frequently. There were only a few disciplinary differences in researchers’ shared file management practices. Some specific design suggestions for PIM tools and applications were made based on the results. This study deepens our understanding of researchers’ shared file management practices in cloud environments, shows possible ways to enhance researchers’ satisfaction, contributes to PIM literature, and informs the design of tools that better support researchers’ shared file management.