An Industrial Digital Financial Application Platform based on Privacy Computing
Xiaobin Zhang, Peng Chen, Chongsong You, Bin Liu, Meilin Huang · 2024
In the digital economy era, data information has become an indispensable production factor and the core asset of enterprises. Therefore, the data privacy protection issue is highlighted. Recently, privacy computing technology has become the focus of both academic and industry. The property of data that can be easily replicated leads an exposed information problem. We aim to build a safe, efficient, active and well-established industrial digital financial application platform for privacy computing. First, the user’s preferences, interests and behaviors are generated based on the analysis of their browsing history. Then, add noise or disturbance to the data, after that, the data is processed through the differential privacy algorithm. This platform contains modules such as data acquisition, data encryption and desensitization, differential privacy protection and so on. The platform utilizes a low-coupling and modular design approach. As a result, the data is available but invisible, and the ownership of data is clear. The data sharing is achieved on the premise of security and privacy.