# The Protection of Data Sharing for Privacy in Financial Vision
###### Abstract
The anonymous Massachusetts Group Insurance Commission (GIC) database is another well-known case and includes the date of birth, gender, and zip code, all linked to voter registration records. The Massachusetts governor's record is fixed [11]. There are other similar publications. In addition to de-anonymization, [PERSON] also identified training sets without public datasets. Nonetheless, the exposed model [12] shows no available training set. Release-only models also have privacy risks. However, the parameters or gradients of the local model are exchanged for the federated learning model. There is also the risk of data leakage [13]. Therefore, additional privacy-preserving mechanisms are required. Many privacy-preserving methods can prevent the re-identification problem of a particular individual's private information caused by comparisons or queries with other databases. The most widely used one is differential privacy [14]. Many federated learning models are now used with differential privacy mechanisms to minimize the risk of data leakage [4; 15]. More businesses are willing to cooperate in the context of personal data protection, creating more significant benefits. Differential privacy was proposed by [PERSON] et al. 2006 [16], reducing the probability of  
Figure 1: The candlestick charts. The red indicates a price increase; the green shows a price decrease. data leakage by adding noise during model training. Training a model using a differential privacy mechanism makes it difficult to compare training data with public data, which is why it provides strong privacy protection. Currently, many well-known companies such as Apple, Google, and Microsoft use differential privacy to apply for data privacy [17]. Traditional financial trading usually relies on traders or experts to judge whether it is bullish or bearish by analyzing candlestick patterns and then deciding whether to buy or sell. With the development of deep learning technology, the topic of financial technology (FinTech) has become a focus of attention. The number of companies offering automated trading or tracking services is also growing. Before building a trade tracking model, it is necessary to consider the choice of indicators, such as the candlestick patterns mentioned above. Experts develop indicators such as sensitive data that can be leaked when trading models are released. The leakage of these sensitive data can cause immeasurable losses to businesses and customers. Because these data are valuable, someone hacks the model to obtain it. Therefore, protection mechanisms are essential. Differential privacy is one of the most popular privacy-preserving mechanisms, but unfortunately, it is rare in finance. The advantage of differential privacy is that it provides strong privacy protection. However, differential privacy protects private data by adding noise and reducing model accuracy. This study compares data security with non-private and personal models of financial vision. It also finds a balance between privacy protection and model accuracy. The results show that when the privacy loss parameter is between 12.62 and 13 5.41, the privacy models can protect training data, and the accuracy does not decrease too much.