Efficient Continuous Big Data Integrity Checking for Decentralized Storage

Cloud computing provides an effective way to manage and share massive medical data for Electronic Medical Record (EMR), hence establishing cloud-based EMR has attracted more and more attention. The data integrity and privacy issue in cloud-based EMR should be emphasized since users do not want their medical records to be damaged or disclosed to others. To address the concern in this paper, we propose an efficient and secure Data Integrity Auditing scheme with Traceability for cloud-based EMR (DIAT), which provides data integrity and privacy. We store multiple copies so that data can be recovered quickly from damage as long as one copy remains intact; meanwhile, data cannot be leaked to unauthorized entities since it is stored as ciphertext. For supporting data dynamics, we have designed a two-dimensional data structure, called Dynamic Mapping Hash Table (DMHT). It does not require large auxiliary validation information, nor does it affect the sequence numbers of other blocks. Moreover, data traceability is achieved by organizing all versions of a data block as a chain so that doctors are enabled to track the changes of patients condition in their records. In addition, formal security analysis and experiment results confirm that DIAT is provably secure and efficient.

INTRODUCTION: With the rapid development of cloud computing and intelligent technology, traditional accounting informatization systems are gradually migrating to cloud computing platforms, promoting financial management's automation and intelligence. OBJECTIVES: However, data security in the cloud computing environment, especially the issue of data integrity, has become one of the core challenges for intelligent accounting informatization systems. Accounting informatization systems rely on the storage and processing of a large amount of financial, tax, and transaction data, and the integrity of these data is directly related to the financial transparency, compliance, and decision-making accuracy of an enterprise. METHODS: Therefore, how to design an efficient and scalable cloud data integrity verification algorithm becomes the key to ensuring the reliability and security of intelligent accounting systems. This paper proposes a cloud data integrity verification algorithm for smart accounting informationization based on a cloud computing environment. The algorithm combines the efficient data processing capability of cloud computing with the automation requirements of smart accounting systems to ensure data integrity during storage, transmission, and processing through various data validation techniques such as digital signatures, hash algorithms, and blockchain-based distributed ledgers. RESULTS: The algorithm design considers the dynamics and scalability of the cloud computing environment. It can realize fast, real-time data validation and integrity detection in a large-scale data environment. Through experimental verification, the algorithm proposed in this paper performs well in processing large-scale accounting data with high verification efficiency and accuracy. CONCLUSION: Compared with traditional validation methods, the algorithm improves the speed of data validation. It enhances the system's ability to prevent data tampering and loss, providing a new cloud data integrity assurance scheme for intelligent accounting informatization systems.

Most of the data generated on high-performance computing systems are transferred to storage in remote systems for various purposes such as backup. To detect data corruption caused by network or storage failures during data transfer, the receiver system verifies data integrity by comparing the checksum of the data. However, the internal operation of the storage device is not sufficiently investigated in the existing end-to-end integrity verification techniques. In this paper, we propose an efficient and robust data integrity verification scheme for large-scale data transfer between computing systems with high-performance storage devices. To ensure the robustness of the integrity verification, we control the order of I/O operations. In addition, we parallelize checksum computing and overlap it with I/O operations to make the integrity verification efficient.

In this paper, we introduce the notion of secret key encryption with non-interactive opening (SKENO). With SKENO, one can make a non-interactive proof π to show that the decryption result of a ciphertext C under a shared secret key K is indeed plaintext M without revealing K itself. SKENO is the secret key analogue of public key encryption with non-interactive opening (PKENO). We give a generic construction of SKENO from verifiable random function (VRF) with certain stronger uniqueness, for example, the Hohenberger–Waters VRF and the Berbain–Gilbert I V-dependent stream cipher construction. Although the strong primitive VRF is used, by taking advantage of the features of the stream cipher, we can still achieve good performance without sacrificing much of the efficiency. Though our VRF-based SKENO construction does not require random oracles, we show that SKENO can be constructed from weak VRF (which is strictly weaker primitive than VRF) in the random oracle model.

SummaryCloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods.

Due to the characteristics of resource-constrained in wireless sensor networks (WSNs), energy consumption is a major concern. Data aggregation is an important method to reduce the energy consumption and communication overhead ; however, it suffers from the security problems of data integrity. In fact, designing integrity schemes for data aggregation is an imperative problem for securing WSNs. In this paper, we propose a secure data aggregation based on homomorphic MAC scheme that provide efficient secure data integrity with different trade-offs between security assumptions, computation cost, and communication payload. The scheme uses two types of homomorphic MAC (i.e., MACs and EMACs). Each MAC provides integrity of the transmitted message and each EMAC provides integrity of the MACs. Our experiments show that our scheme requires less computation overheads and energy consumption than previously methods and can effectively preserve data integrity.

A secure and scalable data integrity auditing scheme based on hyperledger fabric

Blockchain-based Order Management Process in Decentralized Production Networks: A Conceptual Framework

Cloud computing provides a novel computing paradigm for enterprises to store programs and data in the Cloud in a transparent manner, which poses the challenge of security and privacy. In this paper, based on homomorphic cryptography and Zero-Knowledge Proof, we present a novel privacy-preserving scheme for Cloud publish/subscribe service, which achieve efficient privacy-preserving authentication, data integrity, and publish-subscribe confidentiality. The performance evaluation and security analysis demonstrate the practice and validity of the proposed scheme.

Large value hidden in Big Data has motivated enterprises to transform from information technology magnates to data technology magnates. Cloud computing and artificial intelligence offer eligible solutions for extracting information in Big Data, and integration of the above three renders the data cloud platform. In this paper, we specify Transwarp Data Cloud (TDC), a stable, efficient, and intelligent data application cloud platform, which fuses data cloud, analytics cloud, and application cloud to manage all types of applications and services while sharing a well-maintained underlying infrastructure layer. In addition, we identify a general application framework based on TDC, by which various data-driven applications can be supported, and then we discuss some popular applied domains including Big Data, Application Development and Management, and artificial intelligence. With powerful tools and flexible application support, TDC can help enterprises with the innovation and revolution of business in the era of Big Data.

Digital era produces vast amounts of data which leads to advances in data storage and analysis and the emergence of the concept of big data. Big data will be an important resource used in academic research and to make data driven decision making. This paper provides an overview about the big data concept and its application in libraries. This paper aims to create an awareness amongst librarians about big data, its applications and the role of librarian in big data scenario.

Many Big Data analytics essentially explore the relationship among interconnected entities, which are naturally represented as graphs. However, due to the irregular data access patterns in the graph computations, it remains a fundamental challenge to deliver highly efficient solutions for large scale graph analytics. Such inefficiency restricts the utilization of many graph algorithms in Big Data scenarios. To address the performance issues in large scale graph analytics, we develop a graph processing system called System G, which explores efficient graph data organization for parallel computing architectures. We discuss various graph data organizations and their impact on data locality during graph traversals, which results in various cache performance behavior on processor side. In addition, we analyze data parallelism from architecture's perspective and experimentally show the efficiency for System G based graph analytics. We present experimental results for commodity multicore clusters and IBM PERCS supercomputers to illustrate the performance of System G for large scale graph analytics.

The Data Integrity Based Secure Cloud Data Outsourcing Model involves designing a framework that ensures data outsourced to the cloud remains secure, intact, and authentic. Cloud computing has become an indispensable resource for businesses and individuals alike, providing scalable and cost-effective solutions for data storage and management. However, outsourcing data to external cloud service providers (CSPs) raises serious questions around data security, integrity, and authenticity. It is essential to retain confidence and comply with regulatory criteria to make sure that outsourced data is authentic, obtainable, and unaltered. In this article, the Low-Complexity Elliptic Galois Cryptography based Secure Data Integrity Verification Scheme for Cloud Storage (LCEGC-SDIV-CS) is proposed. Initially, the integration of the advantages in Trusted Execution Environment (TEE) and identity-based encryption using Low-Complexity Elliptic Galois Cryptography (LCEGC) is provided to create a safe data integrity verification scheme. Then, data integrity using Block Access Signature Oriented Merle Hash Tree (BS-MHT) is used for the integrity-preserving structure for encrypted block-oriented storage systems. The random Oracle model's security analysis, depending upon the computational Diffie-Hellman assumption, provides security by preventing attacks when the adversary chooses the messages with targets’ identities. The proposed LCEGC-SDIV-CS model is implemented and the performance metrics like Data uploading with downloading time, Memory usage on data uploading with downloading, Encryption time, Decryption time, and Security levels are examined. The proposed LCEGC-SDIV-CS method attains 21.56%, 23.98%, and 21.19% lower data uploading with downloading time 24.78%, 23.82%, and 23.52% lower encryption time are analyzed with existing methods EDI-SPD-SCS, NSA-SADS-BDEPDP-SDS-RCS respectively.

Diverse technologies, such as machine learning and big data, have been driving the prosperity of the Internet of Things (IoT) and the ubiquitous proliferation of IoT devices. Consequently, it is natural that IoT becomes the driving force to meet the increasing demand for frictionless transactions. To secure transactions in IoT, blockchain is widely deployed since it can remove the necessity of a trusted central authority. However, the mainstream blockchain-based IoT payment platforms, dominated by Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms, face several major security and scalability challenges that result in system failures and financial loss. Among the three leading attacks in this scenario, double-spend attacks and long-range attacks threaten the tokens of blockchain users, while eclipse attacks target Denial of Service. To defeat these attacks, a novel bidirectional-linked blockchain (BLB) using chameleon hash functions is proposed, where bidirectional pointers are constructed between blocks. Furthermore, a new committee members auction (CMA) consensus algorithm is designed to improve the security and attack resistance of BLB while guaranteeing high scalability. In CMA, distributed blockchain nodes elect committee members through a verifiable random function. The smart contract uses Shamir's secret-sharing scheme to distribute the trapdoor keys to committee members. To better investigate BLB's resistance against double-spend attacks, an improved Nakamoto's attack analysis is presented. In addition, a modified entropy metric is devised to measure eclipse attack resistance across different consensus algorithms. Extensive evaluation results show the superior resistance against attacks and demonstrate high scalability of BLB compared with current leading paradigms based on PoS and PoW.

By the limitations of storage capacity and computing power, Internet of Things (IoT) devices may prefer to outsource their valuable and sensitive data to cloud storage providers (CSPs) for further analysis, so it is critical to design protocols that can verify the integrity of these data remotely, while preserving the privacy of their owners. This article proposes a novel remote data integrity checking method for IoT, namely, FDUP-remote data integrity checking (RDIC), which achieves fully decentralized, efficient, and unconditionally privacy-preserving checking simultaneously, that is, the proof-checking is performed efficiently on the blockchain by the smart contracts integrated with native C/C++ codes, while the blockchain or any other entity cannot learn any information about the data content, even they have unbounded computing power. Furthermore, it is optimized for low-power IoT devices by greatly reducing the exponentiations of generating homomorphic verifiable tags to be nearly independent of the block size of the outsourced data. To defend against untrusted IoT and CSP, we present strict proofs and analyses in the aspect of correctness, soundness, and unconditionally privacy-preserving. The evaluation of theoretical performance and the prototype system deployed on a blockchain platform indicate that FDUP-RDIC is suitable for real-world IoT applications.