@Article{cmc.2025.062910,
AUTHOR = {Yuanhang Zhang, Xu An Wang, Weiwei Jiang, Mingyu Zhou, Xiaoxuan Xu, Hao Liu},
TITLE = {An Efficient and Secure Data Audit Scheme for Cloud-Based EHRs with Recoverable and Batch Auditing},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {1},
PAGES = {1533--1553},
URL = {http://www.techscience.com/cmc/v83n1/60137},
ISSN = {1546-2226},
ABSTRACT = {Cloud storage, a core component of cloud computing, plays a vital role in the storage and management of data. Electronic Health Records (EHRs), which document users’ health information, are typically stored on cloud servers. However, users’ sensitive data would then become unregulated. In the event of data loss, cloud storage providers might conceal the fact that data has been compromised to protect their reputation and mitigate losses. Ensuring the integrity of data stored in the cloud remains a pressing issue that urgently needs to be addressed. In this paper, we propose a data auditing scheme for cloud-based EHRs that incorporates recoverability and batch auditing, alongside a thorough security and performance evaluation. Our scheme builds upon the indistinguishability-based privacy-preserving auditing approach proposed by Zhou et al. We identify that this scheme is insecure and vulnerable to forgery attacks on data storage proofs. To address these vulnerabilities, we enhanced the auditing process using masking techniques and designed new algorithms to strengthen security. We also provide formal proof of the security of the signature algorithm and the auditing scheme. Furthermore, our results show that our scheme effectively protects user privacy and is resilient against malicious attacks. Experimental results indicate that our scheme is not only secure and efficient but also supports batch auditing of cloud data. Specifically, when auditing 10,000 users, batch auditing reduces computational overhead by 101 s compared to normal auditing.},
DOI = {10.32604/cmc.2025.062910}
}