An Efficient and Secure Data Integrity Auditing Scheme with Traceability for Cloud-Based EMR

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.

Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable downtime due to security threats, such as malicious attacks or system failures, which is unacceptable in many real-time or data-driven applications. As a result, continuous data integrity should be guaranteed in decentralized storage, which ensures that data is intact and available for the entire storage period. However, this requires frequent checking for long time periods and incurs heavy burdens of both communication and computation, especially in a big data scenario. In this paper, we propose an efficient continuous big data integrity checking approach for decentralized storage. We design a data-time sampling strategy that randomly checks the integrity of multiple files at each time slot with high checking probability. Furthermore, to tackle the fairness problem derived from the sampling strategy, we propose a fair approach by designing an arbitration algorithm with the verifiable random function. Security analysis shows the security of our approach under the random oracle model. Evaluation and experiments demonstrate that our approach is more efficient in the big data scenario compared with the state-of-the-arts.

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.

The maintenance and sharing of electronic medical records are one of the essential tasks in the medical treatment combination. Traditional cloud-based electronic medical record storage system is difficult to realize data security sharing. The tamper resistance and traceability of blockchain technology provide the possibility for the sharing of highly sensitive medical data. This paper proposes a safe sharing scheme of stroke electronic medical records based on the consortium blockchain. The scheme adopts the storage method of ciphertext of medical records stored in the cloud and index of medical records stored on the blockchain. The privacy protection mechanism proposed in this paper innovatively combines proxy reencryption and searchable encryption which supports patient pseudoidentity search. The mechanism could achieve controllable sharing of medical records and precise search. According to the organizational characteristics of the stroke medical treatment combination, this paper proposes an improved Practical Byzantine Fault Tolerance mechanism to reach a consensus between consensus nodes. Then, the proposed scheme is analyzed and evaluated from three aspects of medical record integrity, user privacy, and data security. The results show that the scheme can not only ensure the privacy of patient identity information and private key data but also resist the tampering and deletion attacks of internal and external malicious nodes on the medical record data. Therefore, the proposed scheme is conducive to the improvement of the timeliness of stroke treatment and the safe sharing of electronic medical records in stroke medical treatment combination.

Nowadays, Cloud storage helps the users to remotely store and access the data. Any modification or corruption in data by unauthorized users will lead to insecure cloud framework. Protecting the users data from unauthorized access or malicious attacks in cloud is one of the major research direction to address the issues of data privacy and integrity in cloud. Ensuring cloud data integrity and privacy seems to be the major issue. Inorder to overcome unauthorized access of users data even by Cloud Service Provider (CSP), Data integrity verification is performed through Trusted Third Party Auditor (TTPA). By TTPA, the cloud data auditing need to be performed and data security need to be ensured, without the knowledge of the actual data stored in cloud. Many researchers had keen interest to provide a cloud framework, which preserves the privacy and ensures the integrity of cloud data. In this paper, various researcher ideas based on privacy preservation public auditing schemes has been analyzed as an literature review.

Multicopy provable data possession scheme supporting data dynamics for cloud-based Electronic Medical Record system

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.

The rapid digitalization of healthcare systems has led to an unprecedented accumulation of sensitive patient data across various platforms, exposing the industry to growing risks of data breaches, unauthorized access, and integrity compromise. Ensuring the security, privacy, and trustworthiness of healthcare data is paramount to maintaining patient confidentiality and enhancing clinical decision-making. This study proposes a conceptual framework that synergistically integrates Blockchain technology and Artificial Intelligence (AI) to enhance healthcare data security. The framework is designed to address key challenges such as data integrity, access control, real-time threat detection, and secure interoperability across healthcare stakeholders. Blockchain, with its decentralized and immutable ledger capabilities, provides a robust foundation for tamper-proof data storage and transparent audit trails. Smart contracts are employed to automate access controls and ensure compliance with regulatory requirements. AI, on the other hand, plays a critical role in intelligent threat detection and anomaly monitoring. By leveraging machine learning algorithms, the framework can identify suspicious patterns, detect insider threats, and predict potential breaches in real time. The proposed architecture comprises four core components: Secure data ingestion, blockchain-based data storage, AI-powered analytics, and a privacy-preserving access management layer. The framework also incorporates role-based authentication and homomorphic encryption techniques to enhance data privacy while supporting authorized data sharing. Case scenarios such as electronic health record (EHR) exchange and remote patient monitoring are used to demonstrate the practicality and scalability of the model. This integrated approach not only ensures the confidentiality, integrity, and availability of healthcare data but also fosters trust among patients, providers, and regulatory bodies. The framework aligns with global healthcare data standards and complies with regulations such as HIPAA and GDPR. Future directions include the deployment of federated learning models to further decentralize AI training while maintaining data privacy across institutions.

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.

BackgroundNear real-time electronic health record (EHR) data offers significant potential for secondary use in research, operations, and clinical care, yet challenges remain in ensuring data quality and stability. While prior studies have assessed retrospective EHR datasets, few have systematically examined the integrity of real-time data for research readiness.MethodsWe developed an automated benchmarking pipeline to evaluate the stability and completeness of real-time EHR data from the Yale New Haven Health clinical data warehouse, transformed into the OMOP common data model. Twenty-nine weekly snapshots of the EHR collected from July to November 2024 and twenty-two daily snapshots collected from April to May 2025 were analyzed. Benchmarks focused on (1) clinical actions such as patient additions, deletions, and merges; (2) changes in demographic variables (date of birth, gender, race, ethnicity); and (3) stability of discharge information (time and status). A synthetic dataset derived from MIMIC-III was used to validate the benchmarking code prior to large-scale analyses.ResultsBenchmarking revealed frequent updates due to clinical actions and demographic corrections across consecutive snapshots. Demographic changes were most frequently related to race and ethnicity, highlighting potential workflow and data entry inconsistencies. Discharge time and status values demonstrated instability for several days post-encounter, typically reaching a stable state within 4–7 days. These findings indicate that while near real-time EHR data provide valuable insights, the timing of data stabilization is critical for accurate secondary use.ConclusionsThis study demonstrates the feasibility of automated benchmarking to assess the integrity of real-time EHR data and identify when such data become analysis ready. Our findings highlight key challenges for secondary use of dynamic clinical data and provide an automated framework that can be applied across health systems to support high-quality research, surveillance, and clinical trial readiness.

With the rapid development of big data technology and applications, sharing and supervision of massive data have become the demand of industry development. As an emerging technology, blockchain, with excellent characteristics, can promote the prosperity and development of the data‐sharing industry. However, the existing blockchain may lead to some privacy and security issues due to its transparency and lack of supervision. Hence, a secure data sharing model with supervision and privacy protection, named DBSDS, was proposed, which supports illegal content supervision and users revocation, and possesses flexible and fine‐grained access control on data sharing. Firstly, it introduces a dual‐blockchain architecture, one blockchain is used to ensure the integrity and traceability of private data and another is utilized to supervise illegal users so that they can no longer upload and share private data. Secondly, a key tracing tree was built and correspondingly different key generation strategies for different users were designed, which endows regulators with supervision capabilities. Finally, combined ABPER‐KS algorithm, both privacy protection and fine‐grained access control on private data can be accomplished. Furthermore, security analysis and performance comparison manifest that DBSDS is safe and owns better performance, and scheme implementation and experimental analysis indicate the practicality of DBSDS.

Blockchain technology for efficient data management in healthcare system: Opportunity, challenges and future perspectives

This study aims to obtain information on: first, the obligation to create and conceal Electronic Medical Record and its juridical consequences; Secondly, due to the law of absence or error in the manufacture of Electronic Medical Records and the position of Electronic Medical Record as a tool in the theoretical transactions.The research method used is normative juridical approach method, analytical descriptive research specification, research phase is done through literature study to examine primary law material, secondary law material, and tertiary law material. Data collection techniques are conducted through document studies, conducted by reviewing documents on positive law. Furthermore, the method of data analysis is done through normative qualitative.The results of the study indicate: Legal aspects of Medical Record or Electronic Medical Record in Teurapetik Transactions related to: first, the obligation of health workers in coaching and health services to make Medical Record or Electronic Medical Record correctly and responsible for secrecy because it is the opening of Medical Record or Electronic Medical Record without With the permission of the patient having the consequences of criminal law. The absence or misuse of the Medical Record or Electronic Medical Record means that health workers may be subject to criminal, civil and administrative sanctions. Second, the position of Medical Record or Electronic Medical Record is evidence in the form of a letter (if given outside the court), and expert information (if delivered in court).