Integrity Verification Scheme Research Articles

Enhancing Secure and Reliable Data Transfer through Robust Integrity

Cloud computing has emerged as a highly efficient platform that allows multiple users to access various services through virtualization on a shared physical network. The participants in a Cloud Computing (CC) environment include Cloud Service Providers (CSP), Consumers, Brokers, and Auditors. The advantages of cloud storage, such as universal network access, convenience, and scalability, have led to data owners preferring to store their data on remote servers. However, the transfer of outsourced data has become a critical requirement for cloud users due to the availability of different cloud storage services with varying quality of services.
 One major challenge in this context is ensuring the security of secret keys and data integrity. There is no guarantee of data integrity when storing data on an untrusted cloud server. To address this issue, this paper proposes a secure and efficient data integrity verification scheme for cloud storage services. The scheme utilizes a key-homomorphic cryptographic primitive to reduce system complexity and eliminate the need for a public key authentication framework based on a public key infrastructure (PKI) in the data integrity checking protocol. By employing this approach, the proposed method ensures the integrity of remote data stored on cloud servers. Through security analysis and empirical evaluation, it is demonstrated that our scheme is both practical and effective for securely sharing records with multiple owners in cloud computing.

Blockchain based Quantum Resistant Signature Algorithm for Data Integrity Verification in Cloud and Internet of Everything


 INTRODUCTION: The processing and storage capacities of the Internet of Everything (IoE) platform are restricted, but the cloud can readily provide efficient computing resources and scalable storage. The Internet of Everything (IoE) has expanded its capabilities recently by employing cloud resources in multiple ways. Cloud service providers (CSP) offer storage resources where extra data can be stored. These methods can be used to store user data over the CSP while maintaining data integrity and security. The secure storage of data is jeopardized by concerns like malicious system damage, even though the CSP's storage devices are highly centralized. Substantial security advancements have been made recently as a result of using blockchain technology to protect data transported to networks. In addition, the system's inclusive efficacy is enhanced, which lowers costs in comparison to earlier systems.
 OBJECTIVES: The main objective of the study is to a blockchain-based data integrity verification scheme is presented to provide greater scalability and utilization of cloud resources while preventing data from entering the cloud from being corrupted.
 METHODS: In this paper, we propose a novel method of implementing blockchain in order to enhance the security of data stores in cloud.
 RESULTS: The simulations indicate that the proposed approach is more effective in terms of data security and data integrity. Furthermore, the comparative investigation demonstrated that the purported methodology is far more effective and competent than prevailing methodologies.
 CONCLUSIONS: The model evaluations demonstrated that the proposed approach is quite effective in data security.

Kulla‐RIV: A composing model with integrity verification for efficient and reliable data processing services

AbstractThis article presents the design and implementation of a reliable computing virtual container‐based model with integrity verification for data processing strategies named the reliability and integrity verification (RIV) scheme. It has been integrated into a system construction model as well as existing workflow engines (e.g., Kulla and Makeflow) for composing in‐memory systems. In the RIV scheme, the reliability (R) component is in charge of providing an implicit fault tolerance mechanism for the processes of data acquisition and storage that take place in a data processing system. The integrity verification (IV) component is in charge of ensuring that data transmitted/received between two processing stages are correct and are not modified during the transmission process. To show the feasibility of using the RIV scheme, real‐world applications were created by using different distributed and parallel systems to solve use cases of satellite and medical imagery processing. This evaluation revealed encouraging results as some solutions that assumed the cost (overhead) of using the RIV scheme, for example, Kulla (the Kulla‐RIV solution), achieve better response times than others without the RIV scheme (e.g., Makeflow) that remain exposed to the risks caused by to the lack of RIV strategies.

Blockchain assisted blind signature algorithm with data integrity verification scheme

SummaryAs the demand for cloud storage systems increases, ensuring the security and integrity of cloud data becomes a challenge. Data uploaded to cloud systems are vulnerable to numerous sorts of assaults, which must be handled appropriately to avoid data tampering issues. In addition, quantum computers are expected to be introduced soon, which may face multiple security issues by destroying all traditional cryptosystems. This work introduces a quantum‐resistant blockchain centered data integrity verification system with the use of several techniques. Initially, the keys and signatures are generated by the users with the help of the lattice‐based blind signature algorithm (L_BSA), which is a combination of lattice cryptography and a blind signature algorithm. From the generated random keys, the most optimal key is then selected by the Puzzle Optimization Algorithm (POA), which is then made available to the encryption phase. Then, the upgraded Merkle tree‐assisted vacuum filter (Vac‐UMT) algorithm is executed to accomplish the encryption task. Then the data are converted into blocks using blockchain technology and uploaded to the cloud. When receiving the audit requests, the verification process is carried out, and the evidence report is generated for the users. The proposed work is simulated in JAVA and assessed with the UNSW‐NB15 dataset, and the outcomes demonstrated that the system is highly efficient and secure.

Multi-replicas integrity checking scheme with supporting probability audit for cloud-based IoT.

Nowadays, more people are choosing to use cloud storage services to save space and reduce costs. To enhance the durability and persistence, users opt to store important data in the form of multiple copies on cloud servers. However, outsourcing data in the cloud means that it is not directly under the control of users, raising concerns about security and integrity. Recent research has found that most existing multicopy integrity verification schemes can correctly perform integrity verification even when multiple copies are stored on the same Cloud Service Provider (CSP), which clearly deviates from the initial intention of users wanting to store files on multiple CSPs. With these considerations in mind, this paper proposes a scheme for synchronizing the integrity verification of copies, specifically focusing on strongly privacy Internet of Things (IoT) electronic health record (EHR) data. First, the paper addresses the issues present in existing multicopy integrity verification schemes. The scheme incorporates the entity Cloud Service Manager (CSM) to assist in the model construction, and each replica file is accompanied with its corresponding homomorphic verification tag. To handle scenarios where replica files stored on multiple CSPs cannot provide audit proof on time due to objective reasons, the paper introduces a novel approach called probability audit. By incorporating a probability audit, the scheme ensures that replica files are indeed stored on different CSPs and guarantees the normal execution of the public auditing phase. The scheme utilizes identity-based encryption (IBE) for the detailed design, avoiding the additional overhead caused by dealing with complex certificate issues. The proposed scheme can withstand forgery attack, replace attack, and replay attack, demonstrating strong security. The performance analysis demonstrates the feasibility and effectiveness of the scheme.

ValidCNN: A large-scale CNN predictive integrity verification scheme based on zk-SNARK

ValidCNN: A large-scale CNN predictive integrity verification scheme based on zk-SNARK

Physical Unclonable Function-Based Lightweight and Verifiable Data Stream Transmission for Industrial IoT

The deep integration of informatization and industrialization has resulted in an increasingly close connection between supervisory control and data acquisition (SCADA) systems and the Internet, leading to a growing number of new applications in industrial IoT. The boundaries of the SCADA system are monitored by industrial smart sensors, which only have limited security protection and face severe security threats. One of the major threats is that smart sensors are vulnerable to physical attacks because they are often installed in unsafe areas far from plant protection. Under this attack, the data of sensors can be easily tampered with, which may not only mislead the system with corrupted information but also cheat the system to make incorrect decisions. Moreover, since sensors are resource-constrained physical devices, complex and expensive encryption algorithms are not applicable. In this paper, we design a lightweight industrial smart sensor data stream integrity verification scheme based on physical unclonable function (PUF) for industrial IoT, which can protect the physical security of sensors and the integrity of data streams to ensure the secure transmission of industrial smart sensor data streams. We utilize PUF, fuzzy extractor and bit selection algorithm to generate stable PUF responses. A malicious attacker cannot extract the key information through the physical attack. In addition, we design a lightweight integrity verification algorithm with efficient key updating based on lightweight cryptographic primitives, making it suitable for resource-constrained physical devices. We perform the security analysis to demonstrate the security of the scheme to known security vulnerabilities. We implement the proposed scheme and evaluate the performance of our scheme with extensive experiments. The experimental results show that the proposed scheme is efficient in secret key generation and integrity verification and is superior to existing schemes in computational and communication efficiency.

Backdoor-Resistant Public Data Integrity Verification Scheme Based on Smart Contracts

This paper analyzes existing smart contract-based public data integrity verification schemes and identifies certain weaknesses. First, the fair arbitration mechanism deployed in these schemes fails to meet the users’ requirements as it may not promptly notify users of data corruption or loss. Second, to ensure outsourced data confidentiality, existing data integrity schemes use a conventional encrypted method, where each user randomly selects a key to encrypt the outsourced data. Such a method results in varying ciphertexts for the same data by different users, leading to additional storage costs for the cloud server. Third, users’ devices, if poorly designed or even intentionally backdoored, can potentially exfiltrate secrets and compromise the security of schemes. To address these issues, we propose the first backdoor-resistant public data integrity verification scheme based on smart contracts (ASSIST). The key idea is to introduce a new entity (a whistleblower) to periodically monitor the state of verification results recorded in the blockchain. This allows for timely notification of data corruption to users. ASSIST requires users to encrypt their data with a cryptographic primitive called message-locked encryption (MLE), which motivates different users to produce the same ciphertext for the same data and reduces storage costs for cloud servers. We also deploy a cryptographic reverse firewall between users’ devices and the external to re-randomize interactive messages, making the exfiltration impossible. We provide rigorous security proofs to demonstrate the security of ASSIST. The performance evaluation shows that ASSIST is efficient regarding computation and communication costs.

An Efficient and Universal Real-Time Data Integrity Verification Scheme Based on Symmetric Key in Stream Computing System

The integrity of real-time data streams has not been solved for a long time and has gradually become a difficult problem in the field of data security. Most of the current data integrity verification schemes are constructed using cryptographic algorithms with complex computation, which cannot be directly applied to real-time stream computing systems. Aiming at the above issue, this paper adopts the Carter–Wegman MAC method, pseudo-random function and symmetric cryptography mechanism to construct the Real-Time Data Integrity Verification scheme based on symmetric key in stream computing systems (RT-DIV), which converts a one-time MAC to a multiple-time MAC and retains the advantage of security performance. Then, a security analysis is given under the standard model. Finally, experiments and data analysis are conducted in a simulated environment, and the experimental results show that the RT-DIV scheme can effectively guarantee the integrity of real-time data streams. Furthermore, the RT-DIV scheme lays the foundation for the secure application of the stream computing system.

Algebraic Signature-Based Public Data Integrity Batch Verification for Cloud-IoT

With the rapid development of Internet of Things, the related data are growing explosively. However, IoT devices have limited storage and computing capabilities so that they cannot deal with massive data storage and computing locally. The integration of IoT and cloud is regarded as an effective solution to the above issue, i.e., IoT devices outsource collected data to cloud to enjoy powerful storage and computing resources. Because the data stored in cloud are out of the control of IoT users, some security risks need to be addressed in advance. In this paper, we propose a public data integrity verification scheme, called AIVCI, to check the data integrity for Cloud-IoT scenarios. Firstly, based on algebraic signature and homomorphic hash function, AIVCI can efficiently complete data auditing. Secondly, AIVCI adopts blind technology to prevent the privacy leakage of IoT data and further protect the privacy of IoT users. Thirdly, batch auditing is implemented to improve auditing efficiency and meet realistic demands for Cloud-IoT scenarios. And a new data structure named Improved Divide and Conquer Table (ID&CT) is designed to realize efficient data dynamics. Finally, the security and performance analysis demonstrates that AIVCI is more secure and efficient.

Correction: Witanto et al. Distributed Data Integrity Verification Scheme in Multi-Cloud Environment. Sensors 2023, 23, 1623

The authors make the following corrections to the published paper [...]

SM2-Based Offline/Online Efficient Data Integrity Verification Scheme for Multiple Application Scenarios.

With the rapid development of cloud storage and cloud computing technology, users tend to store data in the cloud for more convenient services. In order to ensure the integrity of cloud data, scholars have proposed cloud data integrity verification schemes to protect users' data security. The storage environment of the Internet of Things, in terms of big data and medical big data, demonstrates a stronger demand for data integrity verification schemes, but at the same time, the comprehensive function of data integrity verification schemes is required to be higher. Existing data integrity verification schemes are mostly applied in the cloud storage environment but cannot successfully be applied to the environment of the Internet of Things in the context of big data storage and medical big data storage. To solve this problem when combined with the characteristics and requirements of Internet of Things data storage and medical data storage, we designed an SM2-based offline/online efficient data integrity verification scheme. The resulting scheme uses the SM4 block cryptography algorithm to protect the privacy of the data content and uses a dynamic hash table to realize the dynamic updating of data. Based on the SM2 signature algorithm, the scheme can also realize offline tag generation and batch audits, reducing the computational burden of users. In security proof and efficiency analysis, the scheme has proven to be safe and efficient and can be used in a variety of application scenarios.

Identity-Based Integrity Verification and Public Auditing Scheme in Cloud Storage System Against Malicious Auditors

Identity-Based Integrity Verification and Public Auditing Scheme in Cloud Storage System Against Malicious Auditors

Lightweight certificateless privacy-preserving integrity verification with conditional anonymity for cloud-assisted medical cyber–physical systems

Cloud-assisted medical cyber–physical systems (MCPSs) leverage the power of cloud computing for scalable storage and management of outsourced medical data. Based on the crucial outsourced medical data, guardians could know patient’s health condition timely, doctors can make accurate diagnoses of patients, and medical research centers could also conduct medical big data analysis. However, medical data is highly sensitive and demands strong protection. Meanwhile, the integrity of outsourced medical data is vulnerable to hardware failures, software bugs or human errors. To date, a large number of data privacy-preserving integrity auditing schemes have been presented, but most of them suffer from the burden of complex public key certificate management or the key escrow problem of identity-based cryptography. In this paper, we propose a lightweight certificateless privacy-preserving integrity verification scheme (LCPPIV) with conditional anonymity for cloud-assisted MCPSs by developing an elliptic-curve digital signature and a key exchange mechanism, which can achieve the functionalities of medical data privacy protection, integrity verification of outsourced medical data, conditional anonymity, batch auditing, and secure compensation mechanism, simultaneously. Theoretical security analysis demonstrates that our scheme is provably secure in the random oracle model, with resistance to the public key replacement attack, malicious-but-passive-KGC attack, and response auditing proofs forgery. The performance evaluations indicate that LCPPIV is much more practical for cloud-assisted MCPSs compared with state-of-art data auditing schemes.

A New Data Integrity Verification Scheme with Low Communication Cost

<p>The development of cloud storage is indispensable for today’s Internet information technology. The superiority of cloud storage is that the ownership of the storage medium and the ownership of the stored data are divided and held by two parties with respective interests - cloud server providers and data users. However, the transmission, storage and utilization of data constantly caused the violation of users’ rights and interests. Data integrity as a countermeasure has been paid growing attention to by researchers. Traditional data integrity protection schemes are based on the cloud server-client architecture, which requires high computational overhead and communication. To address this scenario, this paper proposes an improved data integrity verification scheme that enables local users to perform rapid and efficient data integrity verification of data stored on cloud servers, reduces the volume of communication data during the verification process, and effectively prevents the loss or tampering of data stored in the cloud server.</p> <p> </p>

Distributed Data Integrity Verification Scheme in Multi-Cloud Environment.

Most existing data integrity auditing protocols in cloud storage rely on proof of probabilistic data possession. Consequently, the sampling rate of data integrity verification is low to prevent expensive costs to the auditor. However, in the case of a multi-cloud environment, the amount of stored data will be huge. As a result, a higher sampling rate is needed. It will also have an increased cost for the auditor as a consequence. Therefore, this paper proposes a blockchain-based distributed data integrity verification protocol in multi-cloud environments that enables data verification using multi-verifiers. The proposed scheme aims to increase the sampling rate of data verification without increasing the costs significantly. The performance analysis shows that this protocol achieved a lower time consumption required for verification tasks using multi-verifiers than a single verifier. Furthermore, utilizing multi-verifiers also decreases each verifier's computation and communication costs.

Validating the integrity of Convolutional Neural Network predictions based on zero-knowledge proof

Machine Learning as a Service can provide outsourced deep learning model prediction services to clients that do not have computing resources. Meanwhile, the integrity of the prediction services cannot be guaranteed due to model theft attacks from the services provider aspect or fake services from client aspect. Therefore, when clients access such deep learning models, it is urgent to verify the integrity of the model prediction process to ensure trusted interaction. For such, we propose a deep learning integrity verification scheme for integrity verification of the model prediction process without leaking information about key or private parameters of the deep learning model. When conducting integrity verification, the zero-knowledge succinct non-interactive knowledge argument is strategically used to construct a zero-knowledge proof. Specifically, we take the basic structure of the common single-layer Convolutional Neural Networks in deep learning as an example, construct Rank-1 Constraint Satisfaction according to the computational logic of each hidden layer in the model prediction process, so then design the proof circuits. We conduct extensive experiments for different scale CNN model inputs to demonstrate the practicality and feasibility of proposed scheme.

A Data Integrity Verification Scheme for Centralized Database Using Smart Contract and Game Theory

Currently, many companies and institutions use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues because these auditors may be malicious and tamper with or delete user data. This poses a significant challenge for ensuring the reliability of the data verification results. Although introducing a third-party auditor can help address this issue, it may also be untrustworthy and collude with the database service provider to forge false data verification results. In this study, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes blockchain technology as a decentralized third-party auditor, ensuring that the information stored on the blockchain is immutable and cannot be tampered with maliciously. In addition, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not affected by any malicious party. We also leverage game theory to improve the reliability of multiple verifications, reduce verification time and improve overall performance. Our proposed scheme reduces the total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.

Comments on a Dynamic Cloud Data Integrity Verification Scheme: Security Analysis and Improvement

Comments on a Dynamic Cloud Data Integrity Verification Scheme: Security Analysis and Improvement

Comments on a dynamic cloud data integrity verification scheme: security analysis and improvement

To ensure the integrity and reliability of the data on cloud servers, many public data verification schemes have been presented to allow data auditors to remotely audit the data integrity without fetching the data back. Recently, Zhao et al. (2021) proposed a dynamic data integrity audition scheme for cloud computing based on the dynamic array multi-branch tree (MBT). In their scheme, each leaf node x stores a hash value H(x) which relates to all the hash values of blocks in its block array. Because the value H(x) is always used to generate the integrity proof when any block linked to x is challenged, the attacker can deceive the auditor by replacing the challenged block with another one in the same block array. To address this problem of Zhao's scheme, we add a new index i for each block in the block array, which can ensure the uniqueness of each block to resist the replacement attack. Moreover, the algorithms of label generation and proof verification in Zhao's scheme are also upgraded to keep the soundness of the scheme. We give the security proof of the new scheme and make comprehensive performance analyses showing that compared with the original algorithms in Zhao's scheme, only negligible costs such as hash are brought to the new ones.