Exploration of Data Privacy Protection in Financial Risk Assessment Models Based on Computer Vision Technology

This article explores the issue of data privacy when using computer vision technology for financial risk assessment and proposes a new privacy protection mechanism. By analyzing the privacy risks in current computer vision applications, this article evaluates several common data protection technologies and proposes a data encryption strategy based on Convolutional Neural Network (CNN) and differential privacy to enhance the security of financial data. This article evaluates the generalization ability of CNN algorithm and differential privacy model in different financial environments through four experiments. In the first experiment, the impact of the differential privacy budget parameter (ε value) on model performance was explored. The results showed that as the differential privacy budget parameter ε value increased from 0.5 to 10, the accuracy of the model increased from 70% to 90%, and the recall rate increased from 65% to 88%. In the multi-party data fusion security experiment, with the support of enhanced security measures, the accuracy of the CNN algorithm model was improved from 78% for basic security to 87% for advanced security, and the recall rate was increased from 75% to 85%. In multiple model performance comparison experiments, the CNN model showed the highest accuracy of 88% and recall rate of 86% after multiple iterations. In the final generalization ability testing experiment, the accuracy and recall of the CNN model on multiple financial datasets were tested to be very high. Among the four experimental data conclusions, the application value of CNN algorithm models in the field of financial risk assessment also highlights the challenge of balancing model performance and privacy protection in practical applications.