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Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The wide application of sensor networks has brought great challenges to cloud data storage, such as transmission security, data privacy protection and financial risk control. In order to deal with these challenges, the system establishes a comprehensive management early warning system to ensure the data security and financial risk control of the sensor network. The system integrates sensor network technology and cloud data storage technology, and stores data in the cloud through the collection and transmission of environmental data by sensor nodes. In order to ensure the security of the stored process, the system adopts a series of security mechanisms, such as data encryption, access control and identity authentication, and also introduces the financial risk control module, which helps users to warn and manage financial risks through real-time monitoring and analysis of data in the sensor network. The system provides a user-friendly management interface, allowing users to easily configure and monitor the operating state of the sensor network, and supports flexible expansion and customization to adapt to the needs of different scenarios. Through the application of this system, users can effectively manage and control the security and financial risks of sensor networks in cloud data storage, and improve the efficiency and reliability of data storage management.

Cloud has scalable storage and massive computing power, attracting data owners to outsource their data. However, owing to privacy concerns, data is typically encrypted prior to outsourcing, which inevitably presents challenges for querying the data effectively. Although encrypted range query is one of the most prevalent types and has been widely studied, existing schemes still have some problems. They inadvertently disclose the order relationship between the upper/lower bound of a range query and the encrypted index, leading to vulnerability inference attack. Moreover, the cloud server cannot be fully trusted, which may return incorrect and incomplete query results. To deal with these issues, we present a novel verifiable and privacy-preserving range query scheme (VPRQ). The VPRQ scheme utilizes 0/1 technique to transform range comparisons into set intersections. By doing so, it effectively hides the relationship between the upper/lower bound and the encrypted index. On this basis, we design an encrypted garbled bloom filter to securely and effectively achieve range query. This ensures that VPRQ scheme effectively resists inference attack. Additionally, point-value polynomial function technology is integrated into the VPRQ protocol to provide lightweight verification for the query results. Comprehensive security analysis and proof demonstrate its effectiveness in achieving the intended design objectives. Performance evaluations illustrate the feasibility and scalability of the proposed scheme, highlighting its practical applicability. [ABSTRACT FROM AUTHOR]