A Symmetric Projection Space and Adversarial Training Framework for Privacy-Preserving Machine Learning with Improved Computational Efficiency

This paper proposes a data security training framework based on symmetric projection space and adversarial training, aimed at addressing the issues of privacy leakage and computational efficiency encountered by current privacy protection technologies when processing sensitive data. By designing a new projection loss function and combining autoencoders with adversarial training, the proposed method effectively balances privacy protection and model utility. Experimental results show that, for financial time-series data tasks, the model using the projection loss achieves a precision of 0.95, recall of 0.91, and accuracy of 0.93, significantly outperforming the traditional cross-entropy loss. In image data tasks, the projection loss yields a precision of 0.93, recall of 0.90, accuracy of 0.91, and mAP@50 and mAP@75 of 0.91 and 0.90, respectively, demonstrating its strong advantage in complex tasks. Furthermore, experiments on different hardware platforms (Raspberry Pi, Jetson, and NVIDIA 3080 GPU) show that the proposed method performs well on low-computation devices and exhibits significant advantages on high-performance GPUs, particularly in terms of computational efficiency, demonstrating good scalability and efficiency. The experimental results validate the superiority of the proposed method in terms of data privacy protection and computational efficiency.