Data Storage Environment Research Articles

Secure Date Storage Using ECC Encryption with Verifiable Data Sharing in Cloud

Cloud data storage has become increasingly popular, but the security and privacy of data stored in the cloud remain major concerns. Cloud computing is the most feasible approach for implementing Digital Twin services since it has prodigious advantages. It provides on demand services, computing resources, ubiquitous network access, etc., making it suitable for the next-generation information technology architecture. In cloud-assisted data storage environments, the data owners generate data from physical assets and disseminate it to the cloud server.

LIVE PROCESSING AND FEEDBACK ON A COMBINED LASERSCANNING AND PHOTOGRAMMETRIC DOCUMENTATION WORKFLOW

Abstract. The use of a combined terrestrial laserscanning (TLS) and structure-from-motion (SFM) surveying approach has proved to be able to deliver advantageous benefits in terms of coverage and surface rendition. The elaboration of such approaches entails a succession of processes and are without many options for on-site feedback largely dependent on the experience of the surveyor. In support of this approach a combined automated processing workflow with augmented reality feedback is introduced, allowing operators to get a better understanding of their progress and project success during surveying. To do so native TLS and SFM software is connected to a shared network and data storage environment and controlled within an active framework. Within this, scans and images are aligned within RealityCapture and resulting registration is visualised to the operator using augmented reality (AR). To achieve complete independence of pre-scanned environments, the augmented reality application presented within this research uses a GNSS supported visual SLAM algorithm. With this the application is able to position virtual objects – such as markers representing the positions of registered cameras – within a physical space using only their geoposition as reference. The workflow presented here should be understood as a proof of concept to determine possible improvements that would enable such interactive means of surveying and would allow deeper integration of components into a combined AR supported documentation workflow.

Investigation of Efficient Backup Tecniques To Reduce Late In Cloud Systems: A Modeling Study

From the formation stages of the information society to the present day, new developments have been experienced in many areas such as data formation, processing, storage and sharing of data in electronic environments. In this context, many new technological concepts have been included in our lives. When it comes to data storage environments, the concept of "Cloud Computing", which has been developing and increasing in popularity in recent years, comes to our minds. Cloud computing, with its known definition, is an internet-based technology service that provides access to the data stored on its servers at any time through internet access and allows users to benefit from the services offered by the system to the extent they want. While the advantages of cloud computing systems such as low cost, high performance, not requiring physical materials and being flexible can be mentioned, it is also emphasized that it brings many problems in terms of system. The most important of these problems; the lack of resources in cloud computing service providers, causing unpredictable delays in server response times. Based on all this information, this research is structured on the development of solution processes on these delays. In this sense, it is possible to consider three areas of cloud infrastructure; cloud computing, distributed storage and streaming communications. One of the first solutions that comes to mind is duplicating a task on multiple machines and waiting for the earliest copy to finish can reduce service latency; but intuitively, it costs additional computing resources and increases the queue load on servers. In this context, the effect of redundancy on the tail will be analyzed in the first part of the research. As a solution proposal, in this section, it will be discussed that service delays and queue load can be reduced by using the replication method, thus making the system more efficient. In a similar way, by requesting more than one copy of a file and waiting for any of them to arrive, the cloud storage requests will be accelerated for content download. In the second part of the research, generalization will be made from replication to coding and the (n, k) fork-join model will be studied to analyze the delay in accessing a storage system with (n, k) deletion code. This analysis will provide practical information that several users can access a content at the same time and provide faster service to the relevant users. Achieving low latency in streaming communication is even more difficult, as packets must be transmitted quickly and sequentially. Based on this structure, in the third part of the research, it is aimed to develop deletion codes to transmit redundant packet combinations and ensure smooth playback. Experimental modeling method was used as a research method in the study. In general, the aim of the research is to blend various mathematical tools from queuing, theory coding and renewal processes. It is foreseen that the techniques and insights to be developed with these dimensions of the research can be applied to other systems with stochastically changing components, and integrated studies can be made and contribute to the literature in this context.

An Immutable Data Storage using Algorand Blockchain (ABC) Consensus Mechanism in the Supply CHAIN

In recent years, many frameworks and applications have been proposed to ensure tamper resistant data in supply chain using Block chain technologies. Block chain technology as a base design ensures that the content of the information is ‘tamper-resistant’. So far, no other study was presented with a low impact on the environment and minimum cost for each transaction sent by the supply chain. In order to protect the access of malicious user, we tend to propose an immutable data storage environment that is based on Algorand Blockchain. It uses the Pure Proof-of-Stake mechanism of consensus that needs less computational power, and is highly scalable and environmentally sustainable. It will make the data immutable and available in real-time for final consumers. Hence it can tolerate malicious users and achieving consensus without a central authority.

Information Day: Research Infrastructure Services in the Humanities and Social Sciences

During the information day, the results of the work of the IMI-BAS team on the project CLaDA-BG, the Bulgarian National Interdisciplinary Research e-Infrastructure for Resources and Technologies in Favor of the Bulgarian Language and Cultural Heritage, Part of the EU Infrastructures CLARIN and DARIAH, will be presented: the development of the Humanities and Social Sciences Data Storage, Retrieval and Curation Environment (CHCS-DSRCE) and its implementation in the Digital Library "Virtual Encyclopedia of Bulgarian Iconography" and for the needs of the "Ivan Vazov" Regional Library in Plovdiv and “Peyo Yavorov” Regional Library - Burgas.

Distributed big data analysis using spark parallel data processing

Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre-processing steps in a figure of ML pipeline. The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manners have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in the distributed data nodes.

Study on Deduplication on Distributed Cloud Environment

Deduplication methods were designed to destroy copy information which bring about capacity of single duplicates of information as it were. Information Deduplication diminishes the circle space needed to store the back-ups in the extra room, tracks and kill the second duplicate of information inside the capacity unit. It permits as it were one case information event to be put away initially and afterward following occasions will be given reference pointer to the first information put away. In a Big information stockpiling climate, immense measure of information should be secure. For this legitimate administration, work, misrepresentation identification, investigation of information protection is an significant theme to be thought of. This paper inspects and assesses the common deduplication procedures and which are introduced in plain structure. In this review, it was seen that the secrecy and security of information has been undermined at numerous levels in common strategies for deduplication. Albeit much exploration is being done in different zones of distributed computing still work relating to this point is inadequate. To get rid of duplicate data which results in storage of single copies of data, data deduplication techniques were used. Data deduplication helps in decreasing storage capacity requirements and eliminates extra copies of same data inside storage unit. Proper management, work, fraud detection, analysis of data privacy are the topics to be considered in a big data storage environment, since, large amount of data needs to be secure. At many levels in general techniques for deduplication it is observed that safety of data and confidentiality has been compromised. Even though more research is being carried out in different areas of cloud computing still work related to this topic is little.

A Newly Proposed Cloud Computing Based Strengthen Distributed Database System Model and Its Applications in Database Modelling

Abstract: In the modern era of today’s exceptional Information age, the day to day transactions of huge sensitive data sets, which is in the form of PBs (Peta-Bytes) 250 bytes and YBs (Yotta – Bytes) 280 bytes are drastically increases with enormous speed on CLOUD data storage environment. CLOUDs data storage environment are one of the most superior and reliable platform for storing a large sets of data both at enterprise level or local level. Because CLOUD provides online data fetching capability to restore or fetching data at any geographical locations through login their correspondent credentials. But to enhancement or spread of these large data sets are becomes also very complex with respect to maintenance of these data with take concern of consistency and data security, because to maintain these large data sets with full of consistency and integrity are really a very typical and rational tasks, so here In this paper we proposed a distributed database management systems for CLOUD interface also preserves or to take concern data security features with full restoration of CIA (Confidentiality, Integrity, Availability or Authenticity) trade of Information Security. Here we also improvised the mechanisms of traditional distributed database management systems because the tendency to preserves information and recover ability after any misconceptions happens that we restore data which belongs to similar person may have to be stored at different locations, but this newly proposed distributed database systems architecture contains all information or record which belong to similar person are stored in one database rather restore it different databases but the location of these data have to be changes mean while that the content or data which resides in one databases have to be moved to some other database and also preserves the security features, and this model also have capability to run older traditional methodology based distributed database management systems using this model. So the detailed description about these models and communication infrastructure among different CLOUDs are append in the upcoming sections of this paper. Keywords: Cloud based Distributed Database system model, Distributed system, Distributed Database model of CLOUD, Cloud Distributed Database, CLOUD based database systems

Synergy of National Cultural Heritage and Technology

This paper presents the specification of the Humanities and Social Sciences (CHCS) data storage, retrieval and curation environment developed by the IMI-BAS team within the Bulgarian National Interdisciplinary Research E-infrastructure for Bulgarian Language and Cultural Heritage Resources and Technologies (CLaDA-BG). The architecture and functional components of the environment and the technologies used for the development of the environment are presented. The aim of the development is to provide and propose technologies and tools to unify the processes of access, preservation and use of cultural and historical content in accordance with established best practices and regulations in the field.

Mode Optimization and Rule Management of Intellectual Property Rights Protection of Educational Resource Data Based on Machine Learning Algorithm

Educational resource data are a collection of final documents obtained by users, including full-text journals, books, dissertations, newspapers, conference papers, and other database materials. While searching for information in the educational resource database, these resources also have functions such as copying, downloading, reproduction, and dissemination, which raise the issue of expression and protection of intellectual property. Machine learning takes how computers simulate human learning behaviors as the main research content, which can independently determine learning objects, construct their characteristics, perform additional operations beyond the limitations of preset instructions, and discover value from the expression of relative works. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of intellectual property expression and protection of educational resource data; elaborated the development background, current status, and future challenges of machine learning technology; introduced the methods and principles of data classification algorithm and protection authority identification; performed the technical framework design and expression system establishment of the intellectual property expression of educational resource data based on machine learning; analyzed the mode optimization and rule management of intellectual property protection of educational resource data based on machine learning; and finally conducted a simulation experiment and its result analysis. The results show that the machine learning technology can build a subject-oriented, highly integrated, and time-changing educational resource data storage environment; the comprehensive, analysis-oriented decision-supporting system formed by machine learning can give full play to the potential role of data integration and value discovery and is therefore of great significance for the intellectual property expression and protection of integrated and complexly-related educational resource data. The study results of this paper provide a reference for further research on the intellectual property expression and protection of educational resource data based on machine learning.

Superior fuzzy enumeration crop prediction algorithm for big data agriculture applications

Big data cloud application most important data storage and data analysis environment. The big data weather and crop forecasting is a new research agenda in modern agricultural structure and huge data evaluation. This is a challenging research area, as manual inspection of agricultural data can lead to human error. This proposed system's main determination is to improve crop yield prediction accuracy from agricultural big data and design and develop effective programs for data classification. Preprocessing is then used to analyze the collected data and identify the absent values for the attribute. Non-existent values are filled with corresponding new values by checking previous and past results. The fuzzy strategy is the right dimension, reducing the strategy to emphasize any of the self-organizing contours. After reducing the measurement, the reduced dimension information is used to predict climate as a reasonable outcome.An advanced methodology, Fuzzy Enumeration Crop Prediction Algorithm (FECPA) employed for accurate crop yield prediction. Therefore, they can improve the productivity of various sources?(no clarity), data analysis, predictive analysis, crop production, and FECPA with the help of increasing farmers developing a system of integrated data in long trip profit margins. This method shows the final results of experiments that improve the accuracy of yield prediction.

The New Paradigm of Data Protection Law: The Principle of Accountability

The inadequacy of classical data protection approaches have been uncloaked by the evolving nature of cyber risks, the tremendous increase in personal data processing through analytics and artificial intelligence technologies, the diversification of data processing and storage environments and the proliferation of sectoral regulations. The principle of accountability is proposed as the most efficacious solution to tackle new emerging risks and challenges in the changing landscape of data protection and privacy contexts. The principle of accountability is a paradigm shift in data protection which has a conceptual breadth and magnitude that goes far beyond mere compliance. It requires data controllers to implement appropriate and effective measures to comply with the principles and obligations set out under data protection regulations and to further demonstrate this compliance on request. This is a process of proving that the protection of personal data is an essential value that is constantly observed, effectively applied, and regularly audited by data controllers. This article aims to provide a thorough analysis of the principle of accountability in the context of data protection law by adopting a comparative approach. The article aims to scrutinise the scope and underpinnings of the principle, identify its relationship with other data protection principles, and discuss the normative effects of such a principle has on data controllers and data processors.

Data Security for Cloud Datasets With Bloom Filters on Ciphertext Policy Attribute Based Encryption

Cloud data storage environments allow the data providers to store and share large amounts of datasets generated from various resources. However, outsourcing private data to a cloud server is insecure without an efficient access control strategy. Thus, it is important to protect the data and privacy of user with a fine-grained access control policy. In this article, a Bloom Filter-based Ciphertext-Policy Attribute-Based Encryption (BF-CP-ABE) technique is presented to provide data security to cloud datasets with a Linear Secret Sharing Structure (LSSS) access policy. This fine-grained access control scheme hides the whole attribute set in the ciphertext, whereas in previous CP-ABE methods, the attributes are partially hidden in the ciphertext which in turn leaks private information about the user. Since the attribute set of the BF-CP-ABE technique is hidden, bloom filters are used to identify the authorized users during data decryption. The BF-CP-ABE technique is designed to be selective secure under an Indistinguishable-Chosen Plaintext attack and the simulation results show that the communication overhead is significantly reduced with the adopted LSSS access policy.

Automation of voting processes for joint-stock companies using a homomorphic cryptosystem and block-chain technology

In the article is proposed a method for conducting a safe voting among shareholders with the preservation of the secrecy of each vote and obtaining a trusted result on the basis of the use of the Paillier cryptographic system, which implements the additive property of ciphertexts. On the basis of the proposed method, the voting protocol using the blockchain technology was implemented. This technology provides a trusted transaction processing environment. The theoretical concepts of technology of the block transaction chain, the mechanism of homomorphic encryption, distributed key generation are briefly outlined. The step-by-step algorithm of operation of the system is given, the main technical aspects and advantages of using decentralized technologies for data storage and processing are described. Existing decisions in the area of voting systems were investigated and a comparative analysis was made. On the basis of the analysis, the main criteria for holding a safe voting process were formed. The method of distributed key generation among participants of the system is briefly described taking into account the differences between the keys of the Paillier cryptosystem from the standard RSA keys. The paper describes in detail the algorithm of the hierarchical generation of shareholder keys. Using this algorithm allows to optimize the process of storing and processing cryptographic materials on a client application. The feasibility of using smart contract technology, which is a decentralized application with a shared data storage environment and consensus algorithm, was also disclosed and substantiated. Contracts represent an algorithm for processing transaction data in the network, allowing to check the rules of operation of the system and conduct logical operations on the data in a secure way. This method can be used to conduct voting in consortia, joint stock companies and private networks, to be integrated into decentralized data storage systems to provide a secure decision mechanism.

Secure Data Compression Scheme for Scalable Data in Dynamic Data Storage Environments

With the continuous and exponential increase of the number of users and the size of their data, data deduplication becomes more and more a necessity for cloud storage providers. By storing a unique copy of duplicate data, cloud providers greatly reduce their storage and data transfer costs. These huge volumes of data need some practical platforms for the storage, processing and availability and cloud technology offers all the potentials to fulfill these requirements. Data deduplicationis referred to as a strategy offered to data providers to eliminate the duplicate data and keeps only a single unique copy of it for storage space saving purpose. This paper, presents a scheme that permits a more fine-grained trade-off. The intuition is that outsourced data may require different levels of protection, depending on how popular content is shared by many users. A novel idea is presented that differentiates data according to their popularity. Based on this idea, an encryption scheme is designed that guarantees semantic security for unpopular data and also provides the higher level security to the cloud data. This way, data de-duplication can be effective for popular data, whilst semantically secure encryption protects unpopular content. Also, the backup recover system can be used at the time of blocking and also analyze frequent login access system.

Research on Smart Grid Planning and Construction Based on GIS Resource Allocation Technology

Smart grid planning and construction is a very complex engineering project that requires intelligent technology to protect systems such as grid lines and substations to control grid stability and power quality. The smart grid GIS resource allocation technology has designed a new system architecture for the needs of building smart grids. The article expounds the key technologies and typical applications of smart grid GIS resource allocation technology, and describes its broad application prospects in smart grid. As a public service platform for graphical display applications, GIS platform constructs a spatial geographic information data center, which is intelligent. Applications such as grid integration, data mining, and intensive resource utilization provide a massive data storage, management, and analysis environment. The grid GIS platform provides geospatial information sharing and business application integration for smart grid power generation, transmission, substation, power distribution, power consumption, dispatching, etc., providing planning methods and visual analysis services for the construction of smart grid systems.

A probabilistic verification algorithm against spoofing attacks on remote data storage

Remote data storage is a promising service and deployed in large-scale. The verification of data integrity is an important measure to protect the data from data corruption or loss in remote data storage environments. Current verification algorithms are mainly oriented to the service providers who may provide the false verification metadata or force the user to take fewer verification requests owing to the verification expense. To solve these issues, we propose a probabilistic verification algorithm to execute the routine verification and apply the homomorphism-based method to perform the challenge verification. The algorithm improves the metadata generation and innovates the metadata replacement to decrease the cost of verification and avoid the limitation of the traditional hash-based verification only executing for finite rounds. Theoretic analysis and simulation results demonstrate that our algorithm resists the spoofing attack as well as the excessive computation overhead at the expense of less cost in terms of verification data storage, transmission, and computation.

An Optimised Key cryptography for Securing Cloud Data Sharing and Storage Environment

An Optimised Key cryptography for Securing Cloud Data Sharing and Storage Environment

Symmetric Encryption Algorithm to Secure Outsourced Data in Public Cloud Storage

Background/Objectives: Cloud storage is an increasingly popular class of services for archiving, backup and sharing of data. Data security concern has been one of the major hurdles preventing its widespread adoption of cloud. This paper proposes a Symmetric Encryption Algorithm (SEA) for data security in the cloud. Methods/Statistical Analysis: Traditionally, Cryptography techniques are used for data security. Encryption is a cryptography technique used for securing the data. The proposed algorithm in this paper is a symmetric encryption technique. The proposed algorithm executes the ASCII code of each value in the original data. Encryption is done before the data are sent to the cloud. Findings: Proposed SEA is implemented in JAVA and procedure of encryption and decryption of the SEA is evaluated in cloud storage. SEA reduces the time taken for encryption and decryption. Data are frequently uploaded to cloud; proposed SEA should not make latency in data uploading. The proposed algorithm is easily fit into cloud storage environment. SEA helps cloud users and cloud service providers to maintain the security of data in cloud storage. Cloud provider should not access data stored in cloud storage server. Hence, the SEA provides secure data storage environment in cloud. Application/Improvements: Security is more essential to data in the cloud. The proposed SEA provides better security to the data stored in the cloud storage. This technique is suitable to education, medical and agriculture community to securely store their data in cloud storage.

Ensuring Security, Confidentiality and Fine-Grained Data Access Control of Cloud Data Storage Implementation Environment

With the development of cloud computing, the mutual understandability among distributed data access control has become an important issue in the security field of cloud computing. To ensure security, confidentiality and fine-grained data access control of Cloud Data Storage (CDS) environment, we proposed Multi-Agent System (MAS) architecture. This architecture consists of two agents: Cloud Service Provider Agent (CSPA) and Cloud Data Confidentiality Agent (CDConA). CSPA provides a graphical interface to the cloud user that facilitates the access to the services offered by the system. CDConA provides each cloud user by definition and enforcement expressive and flexible access structure as a logic formula over cloud data file attributes. This new access control is named as Formula-Based Cloud Data Access Control (FCDAC). Our proposed FCDAC based on MAS architecture consists of four layers: interface layer, existing access control layer, proposed FCDAC layer and CDS layer as well as four types of entities of Cloud Service Provider (CSP), cloud users, knowledge base and confidentiality policy roles. FCDAC, it’s an access policy determined by our MAS architecture, not by the CSPs. A prototype of our proposed FCDAC scheme is implemented using the Java Agent Development Framework Security (JADE-S). Our results in the practical scenario defined formally in this paper, show the Round Trip Time (RTT) for an agent to travel in our system and measured by the times required for an agent to travel around different number of cloud users before and after implementing FCDAC.