Distributed Cloud Storage Research Articles

Research on the Quality Assessment and Protection of Network Security Body Based on Intelligent Induction and Deep Learning

This mechanism mainly saves the induction information in the distributed cloud storage node, and saves the message summary of the induction information in the block chain node, and then the corresponding relationship between the cloud storage node and the block chain node is saved in the induction information management machine. When the user reads the data, the identity authentication is first completed at the induction information management machine, and the induction information is obtained through the key, and finally the data verification is completed at the block chain node. The main advantages of this mechanism are: using block chain to store intelligent sensing information, and using chain storage to reduce the cost of storage, thus enhancing the scalability of block chain storage. This mechanism uses new hash chains to transmit inductive information, thus improving the security of transmission. Through this study proposes an access control strategy for intelligent sensing information. In this way, the user with the key can quickly complete the work certificate and complete the access, while the illegal intruder who does not hold the key cannot calculate the work certificate of the next block based on the existing block, so he cannot access the intelligence. Under the network topology set in this paper, the model with step 2 is significantly better than step 1 and 3, with 18.30% and 75.01% reduction on MAPE and 15.66% and 87.79% reduction on RMSE. Through hidden Markov, the security situation of the information system under the time series is determined. Through SSIPN, the security event is not used as a single situation assessment index, but the network topology and node vulnerability are included in the assessment scope to enhance the correlation between the security event and each node in the information system. Based on SSIPN, the weight allocation algorithm of the corresponding nodes is proposed, which accurately reflects the impact of the level of the nodes on the network on the overall situation, and realizes the security situation assessment of the overall network.

Efficient and Secure Data Deduplication in Distributed Cloud Storage: A Fault-Tolerant Model Using Active Learning for Big Data Management

As the big data era advances, more and more redundant data are being expressed in various ways. Data deduplication knowledge has never been more important than it is now for lowering redundant data storage and enhancing data quality. Connecting several data tables and identifying distinct entries that point to the same item is typically required, particularly when multi-source data deduplication is involved. Active learning minimises the amount of data that needs to be annotated and trains the classical by choosing the data pieces with the greatest evidence divergence. This approach offers special benefits when handling massive data annotations. Unfortunately, the majority of existing active learning techniques are rarely used for data deduplication jobs and only use classical entity matching. The research effort suggests a model, a distributed cloud storage method that ensures effectiveness, security, and high availability, to close this research gap. By intelligently distributing data subsets among servers, the suggested approach achieves fault tolerance while preserving redundancy for high availability. This suggested model reduces the amount of storage space and upkeep required for the data while eliminating duplicate copies. Additionally, the data is kept in a highly secure and effective manner. The suggested model's efficacy in fault tolerance, cost reduction, batch auditing, and block- and file-level deduplication is demonstrated by the experimental findings. It performs better than current systems thanks to its robust fault tolerance, minimal time complexity, and excellent deduplication capabilities.

Blockchain-Assisted Reputation Mechanism for Distributed Cloud Storage

Distributed cloud storage (DCS) has been undergoing fast development to provide customers with multiple storage resources in a mix of locations and environments that best meet the service and performance requirements. Trust is widely regarded as one of the top obstacles to the adoption and growth of DCS. Nonetheless, the existing works did not take the storage resource capacity of cloud service providers (CSPs) into the reputation evaluation, and most rely on a centralized reputation management framework. We bridge this gap by proposing a blockchain-assisted reputation mechanism for DCS. First, a blockchain-assisted DCS model is proposed to ensure the traceability and tamper proof of reputation-related data. Second, we propose a service credibility quantification method based on rating screening to mitigate the impact of outliers. Third, we design a stochastic process to characterize the storage resource change to quantify CSPs survival probability. Finally, we derive a reputation calculation algorithm based on the above two metrics that protects the authenticity of CSPs' reputations in the presence of attacks. The security analysis and simulation results show that the proposed mechanism is reliable and effective in promoting the service success rate and improving the efficiency and security of the service of DCS.

Quantum-secure fault-tolerant distributed cloud storage system

With the increasing prominence of data security in cloud storage, we propose a practical and robust cloud storage scheme, which uses quantum random numbers as encryption keys, disperses the keys using Shamir’s secret sharing scheme, applies erasure coding to the ciphertext, and securely transmits the data through quantum key distribution protected networks to the distributed clouds. This system offers several key advantages, including quantum-level security, fault tolerance, and storage space saving. To validate its feasibility, we conduct comprehensive experimental tests covering essential functionalities such as encryption/decryption, key preservation, and data storage. By successfully demonstrating the effectiveness of our proposal, we aim to accelerate the application of quantum technology in cloud storage.

Accelerating Distributed Cloud Storage Systems with In-Network Computing

Accelerating Distributed Cloud Storage Systems with In-Network Computing

Secure Distributed Cloud Storage based on the Blockchain Technology and Smart Contracts

Objectives: This paper addresses the problem of secure data storage and sharing over cloud storage infrastructures. A secure, distributed cloud storage structure incorporating the blockchain structure is proposed that supports confidentiality, integrity, and availability. Methods/Analysis: The proposed structure combines two well-known technologies: one of them is the Ethereum Blockchain and its Smart Contracts and the other is the RSA encryption and authentication scheme. The Ethereum Blockchain is used as a data structure, which ensures data availability and integrity while RSA provides sensitive data confidentiality and source authentication. Findings: As a result, users of the proposed structure can trust it and be certain that they can securely exchange information through a publicly accessible and shared cloud storage. The application can be used either through a user interface (UI) or a command-line interface (CLI). Novelty /Improvement:The novelty of this work is that the system that is proposed could be used for secure data storage on the cloud as well as for file sharing and authentication verification. Also, secure data storage and file sharing are already offered by the proposed system. Doi: 10.28991/ESJ-2023-07-02-012 Full Text: PDF

Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

Cloud storage is essential for managing user data to store and retrieve from the distributed data centre. The storage service is distributed as pay a service for accessing the size to collect the data. Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy, duplication leads to increase storage space. The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis. It creates a complex nature to increase the storage consumption under cost. To resolve this problem, this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication (HIBD) based on Segmented Bind Linkage (SBL) Methods for reducing storage in a cloud environment. Initially, preprocessing is done using the sparse augmentation technique. Further, the preprocessed files are segmented into blocks to make Hash-Index. The block of the contents is compared with other files through Semantic Content Source Deduplication (SCSD), which identifies the similar content presence between the file. Based on the content presence count, the Distance Vector Weightage Correlation (DVWC) estimates the document similarity weight, and related files are grouped into a cluster. Finally, the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case. This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.

Privacy-aware genetic algorithm based data security framework for distributed cloud storage

An increased use of data driven applications and integrated systems have caused an accelerating expansion in data volumes and increase in the number of digital records, over the past few decades. Exponentially growing data volumes being processed by large-scale distributed data-intensive applications have placed an increasing pressure on the underlying storage services for timely and efficient storage and retrieval of the data. The use of cloud storage is among the best strategies to efficiently store growing volumes of data. However, outsourcing data to public cloud storage leads to the challenge of data confidentiality preservation. Data Confidentiality is among the top challenges associated with cloud storage which have contributed substantially as an inhibitor for cloud computing adoption all over the world and is considered a serious concern, especially in case of big data, where securing data in a timely and accurate manner is an arduous task. Our study aims to contribute in anti-cybercrime by protecting the confidentiality of sensitive growing data. We enunciate an optimized confidentially preserving framework on distributed cloud storage, that works for growing data with time-efficiency and minimum memory usage. Our framework uses a merger of Genetic Algorithm (GA), parallel data distribution, and privacy-aware selective encryption techniques. The experiments and comparative analysis depict that our proposed framework outperforms others under consideration, in terms of execution time, memory usage and network throughput respectively.

Meaningful Secret Image Sharing with Saliency Detection.

Secret image sharing (SIS), as one of the applications of information theory in information security protection, has been widely used in many areas, such as blockchain, identity authentication and distributed cloud storage. In traditional secret image sharing schemes, noise-like shadows introduce difficulties into shadow management and increase the risk of attacks. Meaningful secret image sharing is thus proposed to solve these problems. Previous meaningful SIS schemes have employed steganography to hide shares into cover images, and their covers are always binary images. These schemes usually include pixel expansion and low visual quality shadows. To improve the shadow quality, we design a meaningful secret image sharing scheme with saliency detection. Saliency detection is used to determine the salient regions of cover images. In our proposed scheme, we improve the quality of salient regions that are sensitive to the human vision system. In this way, we obtain meaningful shadows with better visual quality. Experiment results and comparisons demonstrate the effectiveness of our proposed scheme.

Enterprise Data Sharing with Privacy-Preserved Based on Hyperledger Fabric Blockchain in IIOT's Application.

Internet of Things (IoT) technology is now widely used in energy, healthcare, services, transportation, and other fields. With the increase in industrial equipment (e.g., smart mobile terminals, sensors, and other embedded devices) in the Internet of Things and the advent of Industry 4.0, there has been an explosion of data generated that is characterized by a high volume but small size. How to manage and protect sensitive private data in data sharing has become an urgent issue for enterprises. Traditional data sharing and storage relies on trusted third-party platforms or distributed cloud storage, but these approaches run the risk of single-node failure, and third parties and cloud storage providers can be vulnerable to attacks that can lead to data theft. To solve these problems, this paper proposes a Hyperledger Fabric blockchain-based secure data transfer scheme for enterprises in the Industrial Internet of Things (IIOT). We store raw data in the IIoT in the InterPlanetary File System (IPFS) network after encryption and store the Keyword-index table we designed in Hyperledger Fabric blockchain, and enterprises share the data by querying the Keyword-index table. We use Fabric’s channel mechanism combined with our designed Chaincode to achieve privacy protection and efficient data transmission while using the Elliptic Curve Digital Signature Algorithm (ECDSA) to ensure data integrity. Finally, we performed security analysis and experiments on the proposed scheme, and the results show that overall the data transfer performance in the IPFS network is generally better than the traditional network, In the case of transferring 5 MB file size data, the transmission speed and latency of IPFS are 19.23 mb/s and 0.26 s, respectively, and the IPFS network is almost 4 times faster than the TCP/IP network while taking only a quarter of the time, which is more advantageous when transferring small files, such as data in the IIOT. In addition, our scheme outperforms the blockchain systems mainly used today in terms of both throughput, latency, and system overhead. The average throughput of our solution can reach 110 tps (transactions are executed per second), and the minimum throughput in experimental tests can reach 101 tps.

Public Auditing Scheme for Integrity Verification in Distributed Cloud Storage System

Cloud storage provides a potential solution replacing physical disk drives in terms of prominent outsourcing services. A threaten from an untrusted server affects the security and integrity of the data. However, the major problem between the data integrity and cost of communication and computation is directly proportional to each other. It is hence necessary to develop a model that provides the trade-off between the data integrity and cost metrics in cloud environment. In this paper, we develop an integrity verification mechanism that enables the utilisation of cryptographic solution with algebraic signature. The model utilises elliptic curve digital signature algorithm (ECDSA) to verify the data outsources. The study further resists the malicious attacks including forgery attacks, replacing attacks and replay attacks. The symmetric encryption guarantees the privacy of the data. The simulation is conducted to test the efficacy of the algorithm in maintaining the data integrity with reduced cost. The performance of the entire model is tested against the existing methods in terms of their communication cost, computation cost, and overhead cost. The results of simulation show that the proposed method obtains reduced computational of 0.25% and communication cost of 0.21% than other public auditing schemes.

Search on Encrypted COVID-19 Healthcare Data in Blockchain-Assisted Distributed Cloud Storage

In present circumstances, Coronavirus Disease-2019 (COVID-19) is a very significant health issue affecting human life all over the world. In an Internet of Medical Things (IoMT) environment, the deployed wearable Internet of Things (IoT)-enabled smart devices in a patient's body sense vital information. COVID19-related information is very confidential and private. Thus, the data can be stored into private-blockchain-based distributed cloud storage. For big data analysis, it becomes difficult to decrypt each block residing in the blockchain. Hence, only useful blocks can be searched and then decrypted in order to lessen the computational burden. To deal with this, we discuss a search technique on encrypted COVID-19 data for a blockchain-assisted distributed encrypted database. Finally, blockchain implementation has been also provided to show its effectiveness.

Locally Recoverable Codes on Surfaces

A linear error correcting code is a subspace of a finite-dimensional space over a finite field with a fixed coordinate system. Such a code is said to be locally recoverable with locality r if, for every coordinate, its value at a codeword can be deduced from the value of (certain) r other coordinates of the codeword. These codes have found many recent applications, e.g., to distributed cloud storage. We will discuss the problem of constructing good locally recoverable codes and present some constructions using algebraic surfaces that improve previous constructions and sometimes provide codes that are optimal in a precise sense. The main conceptual contribution of this paper is to consider surfaces fibered over a curve in such a way that each recovery set is constructed from points in a single fiber. This allows us to use the geometry of the fiber to guarantee the local recoverability and use the global geometry of the surface to get a hold on the standard parameters of our codes. We look in detail at situations where the fibers are rational or elliptic curves and provide many examples applying our methods.

Secure biometric authentication with de-duplication on distributed cloud storage.

Cloud computing is one of the evolving fields of technology, which allows storage, access of data, programs, and their execution over the internet with offering a variety of information related services. With cloud information services, it is essential for information to be saved securely and to be distributed safely across numerous users. Cloud information storage has suffered from issues related to information integrity, data security, and information access by unauthenticated users. The distribution and storage of data among several users are highly scalable and cost-efficient but results in data redundancy and security issues. In this article, a biometric authentication scheme is proposed for the requested users to give access permission in a cloud-distributed environment and, at the same time, alleviate data redundancy. To achieve this, a cryptographic technique is used by service providers to generate the bio-key for authentication, which will be accessible only to authenticated users. A Gabor filter with distributed security and encryption using XOR operations is used to generate the proposed bio-key (biometric generated key) and avoid data deduplication in the cloud, ensuring avoidance of data redundancy and security. The proposed method is compared with existing algorithms, such as convergent encryption (CE), leakage resilient (LR), randomized convergent encryption (RCE), secure de-duplication scheme (SDS), to evaluate the de-duplication performance. Our comparative analysis shows that our proposed scheme results in smaller computation and communication costs than existing schemes.

A lightweight privacy-preserving and sharing scheme with dual-blockchain for intelligent pricing system of smart grid

With the deepening of the application of information technology, the development trend of smart grid as the next generation grid has pointed the way for the development of energy internet in the power industry. To protect the privacy of participants, this paper proposes a lightweight dual-blockchain privacy protection and sharing solution for smart grid intelligent pricing systems. We design a lightweight distributed cloud storage architecture based on dual blockchains. The cloud storage architecture stores the encrypted data and returns the address, thereby protecting the privacy of the data. A private blockchain is used to map the association between real identity and pseudonym. At the same time, a new shared blockchain is created for nodes with access control to reduce cost, ensure security and access resource on-demand. Then, secure signature authentication and identity-based proxy re-encryption strategies are designed. Encryption and bilinear pair-based signature scheme is used to aggregate data to support low-cost and high-performance computing. The evaluation results show that the scheme has the advantages of low latency, low response time and small storage cost, and can meet the growing demand for secure communication of full-service data of the smart grid.

Unequal Failure Protection Coding Technique for Distributed Cloud Storage Systems

In recent years, erasure codes have become the de facto standard for data protection in large scale distributed cloud storage systems at the cost of an affordable storage overhead. However, traditional erasure coding schemes, such as Reed-Solomon codes, suffer from high reconstruction cost and I/Os. The recent past has seen a plethora of efforts to optimize the tradeoff between the reconstruction cost, I/Os and storage overhead. Quiet different from all prior studies, in this paper, our erasure coding technique makes the first attempt to take advantage of the unequal failure rates across the disks/nodes to optimize the system reliability and reconstruction performance. Specifically, our proposed technique, the Unequal Failure Protection based Local Reconstruction Code (UFP-LRC) divides the data blocks into several unequal-sized groups with local parities, assigning the data blocks stored on more failure-prone disks/nodes into the smaller-sized group, so as to provide unequal failure protection for each group. In this way, by exploiting the nonuniform local parity degrees, the proposed UFP-LRC enables the data blocks that are stored on more failure-prone disks/nodes to tolerate a greater number of failures while suffering from less repair cost than others, leading to a substantial improvement of the overall reliability and repair performance for cloud storage systems. We perform numerical analysis and build a prototype storage system to verify our approach. The analytical results show that the UFP-LRC technique gradually outperforms LRC along the increase of failure rate ratio. Also, extensive experiments show that, when compared to LRC, UFP-LRC is able to achieve a 10 to 15 percent improvement in throughput, and an 8 to 12 percent reduction in decoding latency, while retaining a comparable overall reliability.

Efficient Replica Migration Scheme for Distributed Cloud Storage Systems

With the wide adoption of large-scale internet services and big data, the cloud has become the ideal environment to satisfy the ever-growing storage demand. In this context, data replication has been touted as the ultimate solution to improve data availability and reduce access time. However, replica management systems usually need to migrate and create a large number of data replicas over time between and within data centers, incurring a large overhead in terms of network load and availability. In this paper, we propose CRANE, an effiCient Replica migrAtion scheme for distributed cloud Storage systEms. CRANE complements any replica placement algorithm by efficiently managing replica creation in geo-distributed infrastructures in order to (1) minimize the time needed to copy the data to the new replica location, (2) avoid network congestion, and (3) ensure the minimum desired availability for the data. Through simulation and experimental results, we show that CRANE provides a sub-optimal solution for the replica migration problem with lower computational complexity than its integer linear program formulation. We also show that, compared to OpenStack Swift, CRANE is able to reduce by up to 60 percent the replica creation and migration time and by up to 50 percent the inter-data center network traffic while ensuring the minimum required data availability.

A novel DNA-inspired encryption strategy for concealing cloud storage

Over the last few years, the need of a cloud environment with the ability to detect illegal behaviours along with a secured data storage capability has increased largely. This study presents such a secured cloud storage framework comprising of a deoxyribonucleic acid (DNA) based encryption key which has been generated to make the framework unbreakable, thus ensuring a better and secured distributed cloud storage environment. Furthermore, this work proposes a novel DNA-based encryption technique inspired by the biological characteristics of DNA and the protein synthesis mechanism. The introduced DNA based model also has an additional advantage of being able to decide on selecting suitable storage servers from an existing pool of storage servers on which the data must be stored. A fuzzy-based technique for order of preference by similarity to ideal solution (TOPSIS) multi-criteria decisionmaking (MCDM) model has been employed to achieve the above-mentioned goal. This can decide the set of suitable storage servers and also results in a reduction in execution time by keeping up the level of security to an improved grade. This study also investigates and analyzes the strength of the proposed S-Box and encryption technique against some standard criteria and benchmarks, such as avalanche effect, correlation coefficient, information entropy, linear probability, and differential probability etc. After the avalanche effect analysis, the average change in cipher-text has been found to be 51.85%. Moreover, thorough security, sensitivity and functionality analysis show that the proposed scheme guarantees high security with robustness.

Proofs of Data Reliability: Verification of reliable data storage with automatic maintenance†

AbstractProofs of Data Reliability are cryptographic protocols that provide assurance to a user that a cloud storage system correctly stores her data and has provisioned sufficient redundancy to be able to guarantee reliable storage service. In this paper, we consider distributed cloud storage systems that make use of erasure codes to guarantee data reliability. We propose two novel Proof of Data Reliability schemes, namely POROS and PORTOS, that on the one hand guarantees the retrieval of the outsourced data in their entirety through the use of proofs of data possession and on the other hand ensures the actual storage of redundancy. As a result, POROS and PORTOS are compatible with the current cloud computing model where the cloud autonomously performs all maintenance operations without any interaction with the user. Moreover, POROS and PORTOS delegate the burden of generating the redundancy to the cloud as well. The security of both solutions is proved in the face of a rational adversary whereby the cheating cloud provider tries to gain storage savings without increasing its total operational cost.

Design and optimization of distributed cloud storage in marine IoT

The traditional cloud storage technology has the defect of little consolidation of actual storage files, due to lack of storage strategy. This paper designs and optimizes the distributed cloud storage technology in the marine IoT. Firstly, we use distributed computing to calculate the node data in the Internet of Things. Then we build a cloud storage framework based on the characteristics of the Internet of Things nodes. Then establish a direct mapping between files and storage strategies in the Internet of Things. Finally, according to the calculated node performance data, a cloud storage strategy is formulated to complete the research on the distributed cloud storage technology in the marine Internet of Things. The experimental results prove that, compared with the traditional cloud storage technology, the distributed cloud storage technology in the marine Internet of Things can finally merge more files when storing files, reducing the storage amount occupied by actual files. It is suitable for practical use.