Financial Vision Based Differential Privacy Applications
This work addresses privacy risks in unsupervised cryptocurrency trading, but it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of data privacy in deep learning applications for cryptocurrency trading by applying two existing differential privacy frameworks (DP-SGD and PATE) to financial trading data. The results show that DP-SGD performs better than PATE with a low privacy-accuracy tradeoff and privacy levels aligned with real-world cases.
The importance of deep learning data privacy has gained significant attention in recent years. It is probably to suffer data breaches when applying deep learning to cryptocurrency that lacks supervision of financial regulatory agencies. However, there is little relative research in the financial area to our best knowledge. We apply two representative deep learning privacy-privacy frameworks proposed by Google to financial trading data. We designed the experiments with several different parameters suggested from the original studies. In addition, we refer the degree of privacy to Google and Apple companies to estimate the results more reasonably. The results show that DP-SGD performs better than the PATE framework in financial trading data. The tradeoff between privacy and accuracy is low in DP-SGD. The degree of privacy also is in line with the actual case. Therefore, we can obtain a strong privacy guarantee with precision to avoid potential financial loss.