# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 1 Introduction
However, the massive use of financial data also contains a large number of potential risks. At the macro level, improper use or leakage of personal or financial institution data will not only directly infringe upon the legitimate rights and interests of individual financial information subjects and affect the normal operation of financial industry institutions, but may also bring about systemic financial risks and threaten financial security, and in serious cases, may spread to the whole society along with the economic chain. At the same time, the occurrence of information infringement and other violations of the law by micro-entities makes it difficult for citizens to complain, and the failure to defend their rights brings huge economic and psychological damage to citizens, which may lead to incidents driven by irrational behaviour, affecting social stability and causing certain damage to the government's credibility. At a micro level, it infringes on the interests of consumers and financial institutions [12; 13]. For consumers, firstly, there is a risk of data misuse and privacy breaches. With the rapid development of digital consumption, financial consumers using financial applications or third-party software for financial services are mandatorily required to allow the right to collect or query information in privacy agreements or various authorisations. All online information of users including identity, location, shopping preferences, payment passwords, and other types of information are recorded in the background. It is not uncommon for financial data to be misused and leaked, by means like over-marketing and big data discriminatory pricing. For financial institutions, firstly, there is the risk of data leakage. Insiders may be bribed to illegally sell or deliberately leak information or be supported by hackers to cause data leakage, which results in losses due to the market going less than expected. Secondly, there is the risk of data contamination. The unlabelled nature of data is highly susceptible to copying and tampering in the course of digital transactions, and once the sample is maliciously damaged, the model results will be very different, which will cause high data clean-up costs for financial institutions, and affect the institutional decision making [14]. Based on the above analysis, we can find that although there are many methods for data encryption and privacy protection, these methods have one or more of the following drawbacks:  
1. It is based on a specific hardware, such as TEE, which makes the method too narrow in scope. 2. The computational complexity of the encryption algorithm is too high, and the computational overhead of encryption is too large. 3. It is not optimised for financial computing scenarios. Many algorithms in the field of finance, such as Monte Carlo algorithms under linear models, n-dimensional discontinuous segmented linear financial market models, financial time series analysis, etc., are linear in their core computation, i.e., matrix multiplication [15; 16; 17; 18; 19; 20; 21]. These computationally intensive operations are suitable for computing in the cloud (e.g., cloud GPUs) [22]. This paper, therefore, proposes a security model for distributed applications that can secure data even if an attacker has physical access to the cloud server. It provides four levels of privacy protection. In the highest level of protection, the server cannot access any information about the user of the data, nor the original text of the data, nor the computational characteristics of the data, such as computational weights and gradients, nor the statistical characteristics of the data, such as the data distribution. Moreover, due to the generality of linear computing in mathematical principles, this model can effectively protect and accelerate all linear computing-based models.