Cloud Data Services Research Articles

Usage of Blockchain Mechanism in Indian Public Administration

Blockchain is an advanced mechanism that allows transparent information for a business network. Normally databases in this technology are stored in blocks that are linked together in a chain. It is used as a facilitator for the process of recording transactions and tracking assets of a business network. It will help share data for a collective group. With blockchain, cloud service and transactional data from multiple sources can be easily collected, integrated, and shared. This technology concerning India presently is used by government banking institutions as well as the government storing and sharing data for a wider group. Blockchain is popular in finance, especially money and time. Recently India has made efforts to become a society with digitalized setup by building a large citizen-scale digital public infrastructure, which is a positive push for the government. The basic motive for introducing a high ended technology like blockchain is for maintaining transparency, and to increase the speed of interactions with individuals. This paper has made attempted to study the mechanism adopted by the Indian government under a blockchain and how well it has been utilized. It has also attempted to give a clear picture, of what all developments have made with blockchain and what sectors are much concentrated with blockchain

Computer Self-Efficacy and Effectiveness of Quipper Learning Management System

Blockchain is an advanced mechanism that allows transparent information for a business network. Normally databases in this technology are stored in blocks that are linked together in a chain. It is used as a facilitator for the process of recording transactions and tracking assets of a business network. It will help share data for a collective group. With blockchain, cloud service and transactional data from multiple sources can be easily collected, integrated, and shared. This technology concerning India presently is used by government banking institutions as well as the government storing and sharing data for a wider group. Blockchain is popular in finance, especially money and time. Recently India has made efforts to become a society with digitalized setup by building a large citizen-scale digital public infrastructure, which is a positive push for the government. The basic motive for introducing a high ended technology like blockchain is for maintaining transparency, and to increase the speed of interactions with individuals. This paper has made attempted to study the mechanism adopted by the Indian government under a blockchain and how well it has been utilized. It has also attempted to give a clear picture, of what all developments have made with blockchain and what sectors are much concentrated with blockchain

Capture Beyond the Platforms: The Material and Infrastructural Conditions for Digital Journalism

News organisations are growing increasingly dependent on digital infrastructures for the distribution of news, AI implementation, and management of data. In this article, we argue that this dependency goes beyond the level of platforms, reaching into the internet backbone infrastructures that transmit, peer, deliver and host data on which journalism relies. Using Norway as a case, we map the actors involved in the subsea cable sectors, internet exchange points, content delivery networks and the data centres and cloud services that traffic, store and process digital journalism. Given the private, foreign nature of most of the actors involved in these infrastructures, we ask how these backbone infrastructures create interdependencies for the news industries in terms of data sovereignty, distribution control and regulatory jurisdiction. We discuss journalism’s dependency on each of these infrastructural sectors using sociotechnical theory, the contribution to which is to extend the range at which platform capture is considered in journalism scholarship.

MIoT-CDPS: Complete decentralized privacy-preserving scheme for medical internet of things

Medical internet of things (MIoT) environments continue to expand, and numerous MIoT services are available to improve the quality of human life. However, these services, facilitated by a multitude of interconnected IoT devices analyzing the environment, face significant security challenges due to the volume of interconnected devices and the exchange of data through open channels. Moreover, the vulnerability is exacerbated by the centralized infrastructure commonly used by IoT services, where compromising a trusted third party (TTP) or service provider jeopardizes the entire system’s security. Existing privacy-preserving schemes for IoT, while aiming for decentralization, often rely on a TTP to sustain the system. In such environments, users typically either entrust their secret keys and parameters to a TTP or store their data in cloud servers. Consequently, as the TTP assumes control over the actual keys and data, a significant challenge arises regarding the assurance of user and data sovereignty. In this paper, we propose a complete decentralized privacy-preserving scheme for MIoT that ensures data self-sovereignty, transparency, and complete decentralization without TTP intervention. In the proposed scheme, users can directly manage their keys with secret parameters. Then, they can upload encrypted data directly to distributed storage and securely share it with other users based on the security levels they have set themselves. We perform informal and formal (mathematical and simulation) security analysis to prove the security of the proposed scheme and conduct comparative analysis to evaluate the security functionalities, communication costs, and computational costs of our scheme compared with those of previous schemes. Further, we implement our scheme in a practical setting through simulations on Ethereum blockchain networks, validating its complete decentralization, security, efficiency, and suitability for MIoT environments. The specificity of our research lies in addressing the critical security concerns inherent in MIoT through a complete decentralized, TTP-independent framework.

An analysis of performance, pricing, and coordination in a supply chain with cloud services: The impact of data security

As an important supporting technology for Industry 4.0 and smart manufacturing, cloud computing technology facilitated the sharing of digital services and reduced energy consumption. However, concerns about data security may cause firms to hesitate to adopt cloud services. How to coordinate their supply and demand and estimate the economic and environmental impacts are not well understood. To address this research gap, this study investigates a supply chain consisting of a cloud-service provider and a manufacturer and a collaboration mechanism that may coordinate both parties’ interests. We employ game theory to design stylized models of two supply chain structures (centralised and decentralised) and derive each firm’s optimal product pricing, profits, and the entire supply chain under different scenarios. The results show that considering cloud-service data security, a cloud-service supply chain cannot achieve coordination in the absence of intervention, while once coordinated it could be more environmentally friendly than the case without coordination. Furthermore, we propose a revenue-sharing contract to incentivise the cloud-service provider to cooperate with the manufacturer and identify the conditions for data security from which both can benefit. Interestingly, enhancing data security does not always help the entire supply chain, and higher data security may worsen it. Finally, numerical examples were derived to demonstrate our conclusions. Our study highlights supply chain coordination in a cloud-service environment and provides significant insights for participants who value data security and carbon–neutral innovation solutions.

A novel multi-step-ahead approach for cloud server aging prediction based on hybrid deep learning model

Predicting software aging in cloud servers is essential for preventing cloud system breakdown and unexpected failures. However, the aging time series data in cloud servers often exhibits non-linearity and stochastic fluctuations, posing challenges for accurate prediction. To address this issue, this study proposes a novel multi-step ahead approach for cloud server aging prediction. Firstly, the original aging time series data is denoised using discrete wavelet transformation (DWT) and then reconstructed into multi-dimensional data by phase state reconstruction (PSR) to capture potential aging patterns. Subsequently, a deep long short-term memory network (DLSTM) is developed to predict the aging trend. Additionally, a stacked auto-encoder LSTM (SAE-LSTM) is designed to optimize the initial weight matrix of DLSTM to enhance prediction abilities. Finally, multi-step prediction experiments are conducted on our time series datasets from OpenStack cloud servers and a public dataset from Alibaba Cluster Trace Program. The comparative analysis of the proposed method with five baseline methods confirms its superiority in terms of prediction accuracy. For response time and free memory test sets, the range of root mean square error (RMSE) values are produced by the proposed method distributed in [0.1688–0.1931] and [139.05–146.05] respectively, with respect to the multi-step ahead prediction horizon from 1 up to 10. These results demonstrate that the proposed method can tackle the limitations of one-step ahead prediction, effectively extracting relevant aging features, and achieving low prediction accuracy. The study provides valuable insights into revealing future aging trends in cloud servers and offers a reference for implementing preventive maintenance and prognostics in cloud servers.

A Framework on Group Data Sharing Based Agreement Implementation in Cloud Computing

Abstract -- Cloud computing and cloud storage have become hot topics in recent decades. Both are changing the way we live and greatly improving production efficiency in some areas. At present, due to limited storage resources and the requirement for convenient access, we prefer to store all types of data in cloud servers, which is also a good option for companies and organizations to avoid the overhead of deploying and maintaining equipment when data are stored locally. The cloud server provides an open and convenient storage platform for individuals and organizations, but it also introduces security problems. Due to the particularity of some industries, some enterprises have extremely high requirements on the security of data. If the data is lost or destroyed, the enterprise will bear serious consequences. The second problem is reliability. Only by ensuring that the data records are accurate and reliable can the data transmission be more secure and the enterprise win a higher reputation. In addition, the issue of cloud computing regulation is also very important. Only through efficient and orderly regulation can enterprises operate healthily and normally. At present, many small and medium sized enterprises need to spend huge costs on data storage, which will continue to increase with the expansion of data volume. However, the emergence of cloud storage services has brought the dawn for these enterprises, they will need to store the data transmission to the cloud, through the cloud storage service provider to solve this problem, so that they can save the cost of their own data center, use low price to achieve the purpose of data storage.

Security Improvements of JPEG Images Using Image De-Identification

Today, as data is easily exposed through various channels, such as storing data in cloud services or exchanging data through a SNS (Social Network Service), related privacy issues are receiving a significant amount of attention. In addition, for data that are more sensitive to personal information, such as medical images, more attention should be paid to privacy protection. De-identification is a common method for privacy protection. Typically, it is a method of deleting or masking individual identifiers and omitting quasi-identifiers such as birth dates. In the case of images, de-identification is performed by mosaic processing or applying various effects. In this paper, we present a method of de-identifying an image by encrypting only some of the data in the JPEG (Joint Photograph Experts Group) image format, one of the most common image compression formats, so that the entire image cannot be recognized. The purpose of this paper is to protect images by encrypting only small parts, and not the entire image. This work is suitable for the fast and safe transmission and verification of high-capacity images. We have shown that images can be de-identified by encrypting data from the DHT (Define Huffman Table) segment among the JPEG header segments. Through experiments, we confirmed that that these images could not be identified after encrypting only a minimal portion, compared to previous studies that encrypted entire images, and the encryption speed and decryption speed were also faster and more effective than the results of previous studies. A model was implemented to de-identify images using AES-256 (Advanced Encryption Standard-256) and symmetric key encryption algorithm in the Huffman tables of JPEG headers, resulting in the ability to render entire images unidentifiable quickly and effectively.

The Role of Cloud Storage in Rebuilding Governmental Data After Conflict: A Case Study of Sudan 2023

The aim of this research is to assess the Role of Cloud Storage in Rebuilding Governmental Data After Conflict, with a deeper insight into Sudanese case. To investigate the role of cloud storage in rebuilding governmental data after conflict in Sudan, this study employs a quantitative research methodology. The primary data collection instrument is a structured questionnaire designed to gather insights and perspectives from 80 participants of key stakeholders involved in the cloud storage adoption and data reconstruction efforts in Sudan, including (IT professionals and data management experts, Senior-level government officials and policymakers, Representatives from cloud service providers, End-users and data consumers from various government agencies). The collected data underwent accurate statistical analysis, including descriptive and inferential techniques, to identify patterns, trends, and relationships among the variables under investigation. Quantitative data was analyzed using "SPSS" statistical software. The findings of the research revealed that, a total of 18 participants representing (22.5%) agree that there is a high level of awareness and understanding of cloud storage technologies among government agencies in Sudan, while 28 participants representing (35%) disagree. Which indicates the need of spreading the awareness and understanding of cloud storage technologies among government agencies in Sudan. Furthermore, Pearson Chi-Square test was used to measure the role of cloud storage in rebuilding governmental data after conflict in Sudan, a significance value of (0.021) which is less than (p-value=.05) suggests that there is a significant role of cloud storage in rebuilding governmental data after conflict in Sudan.

Innovative Approaches to Full-Text Search with Solr and Lucene

Full-text search engines help efficiently process large volumes of textual material and provide appropriate results. Apache Solr and Apache Lucene are popular full-text search tools for indexing and querying huge datasets. This research study examines Solr and Lucene's strengths, weaknesses, and unique methods for improving full-text search efficiency and accuracy. Apache Lucene, the fundamental full-text search framework, has extensive indexing and querying features. Developers may tailor the search process using its flexible and extendable framework. Advanced indexing algorithms like inverted indices and tokenization underpin Lucene's search capabilities. However, complicated query needs and efficient large-scale data management remain problems. Solr, founded on Lucene, adds faceting, distributed searching, and rich text analysis to its search engine. Enterprise applications may use Solr's high availability, fault tolerance, and large-scale deployments. Solr has performance tuning and setup complexity issues despite these benefits. This study explores novel solutions to these issues and improves full-text search. Advanced tokenization and normalization may improve indexing tactics. Machine learning algorithms increase search relevancy, providing more accurate and contextual results. Query processing optimization is another invention. Caching, query rewriting, and parallel processing may minimize query latency and boost throughput. GPUs are also used to improve query execution. The article also discusses integrating Solr and Lucene with big data platforms and cloud services. Distributed computing frameworks and cloud storage may improve scalability and real-time search. How Solr and Lucene may incorporate AI and NLP to improve search accuracy and user experience is also investigated.

Innovating Household Food Waste Management: A User-Centric Approach with AHP-TRIZ Integration.

Food waste management remains a paramount issue in the field of social innovation. While government-led public recycling measures are important, the untapped role of residents in food waste management at the household level also demands attention. This study aims to propose the design of a smart system that leverages sensors, mobile terminals, and cloud data services to facilitate food waste reduction. Unlike conventional solutions that rely on mechanical and biological technologies, the proposed system adopts a user-centric approach. By integrating the analytical hierarchy process and the theory of inventive problem solving, this study delves into users' actual needs and explores intelligent solutions that are alternatives to traditional approaches to address conflicts in the problem solving phase. The study identifies five main criteria for user demands and highlights user-preferred subcriteria. It determines two physical conflicts and two technical conflicts and explores corresponding information and communications technology (ICT)-related solutions. The tangible outcomes encompass a semi-automated recycling product, a mobile application, and a data centre, which are all designed to help residents navigate the challenges regarding food waste resource utilisation. This study provides an approach that considers users' genuine demands, empowering them to actively engage in and become practitioners of household food waste reduction. The findings serve as valuable references for similar smart home management systems, providing insights to guide future developments.

Platform Supporting Intelligent Human–Machine Interface (HMI) Applications for Smart Machine Tools

As the Internet of Things, artificial intelligence, and the fourth industrial revolution advance, smart factories and machines increasingly gain intelligent features that enable the integration of more sophisticated functionalities. Approaches to achieving this intelligence involve both internal systems, such as human–machine interface (HMI), and external systems, such as big data platforms and cloud services. Although current research leans toward studying external systems, accomplishing intelligent functions through such means poses more challenges in achieving real-time responses during machining processes than using internal systems. When intellectualizing machine tools through internal HMI systems, three critical issues must be addressed. First, HMI functions are structured to depend on the HMI itself, leading to a ripple effect where a problem occurring in one HMI function impacts the entire system. Second, owing to differences in development tools and programming languages, the interconnectivity between functions developed by multiple stakeholders to be loaded onto the HMI may suffer, leading to potential inefficiencies and increased maintenance costs. Third, although various types of computer numerical control (CNC) machines need to communicate with the HMI function, the diverse communication methods and development tools used by each CNC manufacturer result in identical intelligent functions being developed separately for each CNC type. To address these challenges, this study proposes an innovative HMI platform capable of executing and developing various intelligent functions. The HMI platform and its major components are designed and implemented through component-based development (CBD). Subsequently, the performance and effectiveness of the platform are validated using quality attribute scenarios.

Privacy by design. User-held data model as a basis for Privacy and Personal Data protection in social networks

The article reveals the concept and features of privacy by design approach. An analysis of the legislation of the European Union, which regulates the obligation to comply with mentioned concept, namely the provisions of the General Data Protection Regulation, was carried out. Within the framework of the concept of privacy by design, the concepts and pecularities of using user-held data model are studied, which aims to change the legal regulation and technical support of information security as well as the security of personal data in social networks.
 In order to achieve the goal of the research, the need to introduce the specified model of data protection was analyzed – namely practical problems of personal data protection within the peculiarities of the market of social network service providers. The need of the interpretation of personal data as an object of property right was outlined, as well as the interpretation of the purpose of the meta data itself as personal information about a person that can be used to recognize or track a person separately or in combination with other information, which is directly stated in the OMB Act No. A-130, however, is neither interpreted similarly in the Data Protection Regulation nor in the legislation of Ukraine. The main advantages of implementing a user-held data model are presented, namely the advantages for data owners, businesses, the state, and cloud service providers for providing and maintaining the model.
 Legislation in the field of personal data protection in the EU, in particular in the field of social networks, is more complex at the supranational and national level. It is provided with a separate regulatory and organizational mechanisms, which are different from the one that exists today in Ukraine. The mechanism for the protection of personal data in the European Union provides the creation of independent public authority that is responsible for the implementation and compliance with the requirements of the General Data Protection Regulation. The article highlights the need to create a single separate body in the field of personal data protection in Ukraine, which will be able to perform the function of monitoring and ensuring compliance with the requirements of the relevant legislation, in particular, compliance with privacy by design.

Mitigating Carbon Footprint through Green Software: A Comprehensive Framework for Energy-Efficient Data Centers and User-Friendly Carbon Footprint Calculation

The carbon footprint of the world is increasing day by day by the activities caused by every citizen. This is because the demand for energy increases and due to the lack of awareness.The average carbon emission is around 4.72 tonnes per person. There are some free online tools which are available for calculating the carbon footprint of each citizen. But those work as a datacentre in which data must be entered manually by the user which makes it quite inefficient. The existing methods do not meet the requirement of ease of handling the data.This software and the datacentres were developed which includes the availability of a user-friendly interface.Data centers also play a major role in carbon footprint, they emit CO2 and consume more energy. Data centers and new technology adoptions are mainly causing this carbon emission.These Data Centres use cloud energy to serve the user-generated requests and this energy consumption is the basic cause of carbon emission. Therefore, the energy efficiency of the data center must be governed. To reduce it, the concept of green software is introduced. Though hardware is seen as the main culprit for the consumption of energy, software plays a tremendous role in determining the efficiency of the hardware. Therefore, software must be energy efficient to handle the data center in low cost, low consumption, and low pressure.This led to the development of GREEN Software. The implementation of green software involves various factors.To reduce it, data center simulators are being used. This simulator calculates the amount of energy consumed and the amount of CO2 emitted, which as a result computes the change in carbon footprint due to it. This green software design framework provides a separate track session on energy consumption by the data center and cloud service provider along with the acquired energy efficiency level by the specific center.

Improved secure PCA and LDA algorithms for intelligent computing in IoT‐to‐cloud setting

AbstractThe rapid development of new technologies such as artificial intelligence and big data analysis requires the simultaneous development of cloud computing technology. The application of IoT‐to‐cloud setting has been fully applied in various industry sectors, such as sensor‐cloud system which is composed of wireless sensor network and cloud computing technology. With the increasing amount and types of collected data, companies need to reduce the dimension of massive data in cloud servers for obtaining data analysis reports rapidly. Due to frequent cloud server data leaks, companies must adequately protect the privacy of some confidential data. To this end, we designed a dimension reduction method for ciphertext data in the sensor‐cloud system based on the CKKS encryption scheme, principal component analysis (PCA) and linear discriminant analysis (LDA) dimension reduction algorithm. As data cannot be directly calculated using traditional PCA and LDA algorithm after encryption, we add some interactive operations and iterative calculations to replace some steps in traditional algorithms. Finally, we select the classification dataset IRIS which is commonly used in machine learning, and screen out the best encryption and calculation parameters, and efficiently realize the dimension reduction method of ciphertext data through a large number of experiments.

Patient-Centric Token-Based Healthcare Blockchain Implementation Using Secure Internet of Medical Things

Security and privacy are becoming increasingly difficult to preserve in today’s fast-growing data world. We use the Internet of Medical Things (IoMT) to make quick diagnoses and get results. IoMT devices measure the human body’s functioning in various parameters and collect, process, and store data in cloud servers. There is a public channel used to collect data from humans and send it to the cloud, which is highly unreliable and vulnerable to attacks. Another risk is storing data in a single centralized system, which can be vulnerable to a single-point failure. Blockchain is being used for secure data storage in a decentralized manner to avoid single-point failure. We propose a secure IoMT-based data collection method for patients and storing the data on the blockchain in accordance with general data protection regulation (GDPR). In the proposed system, IoMT devices send data to the cloud via the patient’s personal digital assistant (PDA), and the cloud server transacts data on the blockchain. We propose a miner selection algorithm to avoid bias in the blockchain. We simulate various attacks on open channels between IoMT devices and the cloud servers. This scheme is implemented on a custom Python-based blockchain. IoT simulation is performed using the Bevywise IoT simulator and the message queuing telemetry transport (MQTT) simulator. The security protocol is analyzed using Scyther.

Trust Model for Cloud Data Service Providers

Trust Model for Cloud Data Service Providers

Efficient time-oriented latency-based secure data encryption for cloud storage

To ensure data security in the cloud, there exist several techniques proposed by various researchers. The most common method is the usage of data encryption techniques like profile, rule, and attribute-based encryption techniques. However, they struggle in achieving higher data security performance due to insufficient resistance to tampering. Also, the existing techniques are not good enough for achieving a higher quality of service performance. To handle this issue, an efficient time-oriented latency approximation-based data encryption technique (TLADE) has been presented in this article. The method focuses on the selection of optimal encryption techniques at different time stamps according to the latency approximation. Accordingly, the method would select an optimal technique for data encryption based on the QoS values. To perform this, different encryption schemes have been implemented and each of them is measured for their QoS support values (QoSV) based on latency. Based on the values of QoSV, an efficient approach for the current duty cycle has been selected and applied to the cloud service data. The proposed approach improves the performance of different QoS factors and also has reduced the latency factor.

IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey

Geothermal energy installations are becoming increasingly common in new city developments and renovations. With a broad range of technological applications and improvements in this field, the demand for suitable monitoring technologies and control processes for geothermal energy installations is also growing. This article identifies opportunities for the future development and deployment of IoT sensors applied to geothermal energy installations. The first part of the survey describes the technologies and applications of various sensor types. Sensors that monitor temperature, flow rate and other mechanical parameters are presented with a technological background and their potential applications. The second part of the article surveys Internet-of-Things (IoT), communication technology and cloud solutions applicable to geothermal energy monitoring, with a focus on IoT node designs, data transmission technologies and cloud services. Energy harvesting technologies and edge computing methods are also reviewed. The survey concludes with a discussion of research challenges and an outline of new areas of application for monitoring geothermal installations and innovating technologies to produce IoT sensor solutions.

The LOFT Attack: Overflowing SDN Flow Tables at a Low Rate

The emerging Software-Defined Networking (SDN) is being adopted by data centers and cloud service providers to enable flexible control. Meanwhile, the current SDN design brings new vulnerabilities. In this paper, we explore a stealthy attack that uses a minimum rate of attack packets to disrupt SDN data plane. To achieve this, we propose the LOFT attack that computes the lower bound of attack rate to overflow flow tables based on the inferred network configurations. Particularly, each attack packet always triggers or maintains consumption of one flow rule. LOFT can ensure the attack effect under various network configurations while reducing the possibility of being captured. We demonstrate its feasibility and effectiveness in a real SDN testbed consisting of commercial hardware switches. The experimental results show that LOFT incurs significant network performance degradation and potential network DoS at an attack rate of only tens of Kbps. To defeat the attack, we develop a data-to-control plane collaborative defense system named LOFTGuard, which is lightweight and transparent to SDN applications. Evaluations show that Guard effectively protects SDN against the attack and introduces a small overhead.