Analyzing Enterprise Asset Structure and Management Capability Using Cloud Computing and Industrial Enterprise Financial Accounting Cost Accounting Methods

This study aims to investigate the integration of cloud computing technology within the financial cost accounting framework of industrial enterprises to enhance asset structure and management capabilities. It addresses critical challenges, including low accuracy, inefficiency, and data leakage prevalent in enterprise financial cost accounting. The study proposes a multi-user cloud financial data privacy protection and classification model based on Support Vector Machine (SVM) techniques. Initially, the model preprocesses financial data and employs a distributed two-trapdoors public-key cryptosystem (DT-PKC) for encryption, ensuring secure data uploads to the cloud platform. Subsequently, it manages multi-user keys via a trusted third-party key distribution center, thereby reducing communication costs and enhancing privacy protection. The SVM model is then extended into the encrypted domain, utilizing user public keys to safeguard sensitive information from leakage. Finally, the model trains and predicts on encrypted data using SVM, returning the results to the enterprise for decryption and informed decision-making. Performance analysis reveals that the proposed algorithm demonstrates significant improvements in financial data processing efficiency (with a runtime enhancement exceeding 10%) and accuracy (exceeding 95%) when compared to the baseline K-Nearest Neighbors (KNN) algorithm, while maintaining communication overhead below 200Kb. Consequently, this model markedly enhances the efficiency and accuracy of financial data processing, thereby strengthening enterprise asset structure and management capabilities.