# The Protection of Data Sharing for Privacy in Financial Vision
###### Abstract  
The primary motivation is to address difficulties in data interpretation or a reduction in model accuracy. Although differential privacy can provide data privacy guarantees, it also creates problems. Thus, we need to consider the noise setting for differential privacy is currently inconclusive. This paper's main contribution is finding a balance between privacy and accuracy. The training data of deep learning models may contain private or sensitive corporate information. These may be dangerous to attacks, leading to privacy data leakage for data sharing. Many strategies are for privacy protection, and differential privacy is the most widely applied one. Google proposed a federated learning technology to solve the problem of data silos in 2016. The technology can share information without exchanging original data and has made significant progress in the medical field. However, there is still the risk of data leakage in federated learning; thus, many models are now used with differential privacy mechanisms to minimize the risk. The data in the financial field are similar to medical data, which contains a substantial amount of personal data. The leakage may cause uncontrollable consequences, making data exchange and sharing difficult. Let us suppose that differential privacy applies to the financial field. Financial institutions can provide customers with higher value and personalized services and automate credit scoring and risk management. Unfortunately, the economic area rarely applies differential privacy and attains no consensus on parameter settings. This study compares data security with non-private and differential privacy financial visual models. The paper finds a balance between privacy protection with model accuracy. The results show that when the privacy loss parameter \(\epsilon\) is between 12.62 and 5.41, the privacy models can protect training data, and the accuracy does not decrease too much. financial vision; Gramian Angular Field (GAF); differential privacy; private aggregation of teacher ensembles (PATE); differentially private stochastic gradient descent (DP-SGD) +
Footnote †: journal: Applied Sciences  
Federated learning can be applied to finance. Data in the economic domain are similar to medical data. The value of data across agencies can be significantly enhanced by combining them. Financial institutions can provide customers with higher-value and personalized services, such as automated tracking and trading in stock or currency markets. AI models can also automate credit scoring and risk management and solve business challenges such as fraud [3; 4]. However, most of these data contain a substantial amount of personal data. Leaks can have uncontrollable consequences, making data exchange and sharing difficult. With federated learning techniques, financial institutions can collaborate without exchanging private information [5]. Several companies such as IBM [6], Microsoft [7], or Amazon [8] have applied federated learning to provide customers with secure data analysis and AI model training to effectively mine potential value. For example, we now have some candlestick charts (Figure 1). Each of them represents a signal that may be bullish or bearish. These signals need to be labeled by experts, and then we can use these labeled data to build signal detection models. Therefore, data are sensitive information. However, the data labeled by each institution are limited. Data can maximize its benefits if information can be shared using federated learning techniques. Privacy protection is a crucial issue today, and many studies have considered the balance between big data and data privacy when conducting research. K-anonymity [9] is easy to understand but not sufficient for protecting private data. There are some success stories of unlocking anonymous data sets by using public data sets. For example, de-anonymized Netflix data were successfully studied by comparing Netflix's anonymized viewing history with IMDb data [10].