Progress in Machines and Systems Hajar Ait Lamkademe 15 1 2026 https://doi.org/10.6025/pms/2026/15/1/19-33 https://www.dline.info/pms/fulltext/v15n1/pmsv15n1_2.pdf With the rapid development of information technology and the widespread deployment of database systems, massive volumes of enterprise data are generated and stored every day. These datasets contain valuable information that can support strategic decision-making and business intelligence. However, migrating accounting systems to cloud environments introduces significant challenges for data integrity, confidentiality, and security. Ensuring that financial data stored in distributed cloud infrastructures remains accurate, complete, and tamper free has become a critical issue for modern accounting information systems. To address this problem, this study proposes a cloud-based algorithm for verifying the integrity of accounting data that integrates rough set theory with data mining techniques. The proposed approach establishes a secure and reliable cloud storage architecture capable of performing efficient data verification through parallel processing. By incorporating rough set-based attribute reduction and feature selection mechanisms, the algorithm improves computational efficiency while maintaining high accuracy in detecting data corruption or inconsistencies. In addition, the proposed system supports both single user and multi user verification mechanisms, enabling scalable data auditing in cloud environments. Experimental evaluation demonstrates that the proposed parallel verification algorithm significantly reduces communication overhead and verification time compared with traditional single-user verification schemes. The results further show that the algorithm maintains high verification accuracy while optimising computational resource utilisation. Therefore, the proposed method provides a reliable and efficient solution for safeguarding the integrity of accounting information in cloud computing environments.