# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 6 Conclusions  
With the continuous increase and upgrade of the financial system, more and more financial entities are interconnected in numerous ways, which brings great challenges to information security because financial information will be exposed to a variety of potential threats. In this paper, we proposes a privacy protection model based on cloud computing that can ensure data security even if an attacker has physical access to the cloud server. The model provides four levels of privacy protection according to users' actual needs. In the highest level of protection, the server has no access to any information about the data user, nor does it have access to the original file information of the data, nor can it restore the computational characteristics of the data, such as computational weights and gradients, and statistical characteristics of the data, such as data distribution. In addition, due to the generality of the mathematical principles of linear operator, the model can effectively protect and accelerate all models based on linear operations. The main innovations of this document are as follows:  
1. The paper proposes a computing framework based on client-side encryption and decryption, accelerated computing in the cloud, and designs multiple privacy protection levels.
2. The paper adapts framework to many algorithms in the field of finance, such as Monte Carlo algorithm under linear model, n-dimensional discontinuous linear financial market model, and financial time-series analysis.  
Figure 6: Data distribution of the attacker brute force at 0.1% data jitter.  
The paper designs a scheduling model for multi-client shared cloud GPU.
* 4. The paper has implemented a large number of experiments to verify the effectiveness of this method.  
The final results showed that the method can increase the speed by 10 times, compared with the privacy protection method that only uses local computing power instead of the cloud server. It can also effectively prevent the user's privacy from being leaked with relatively minimal delay cost, compared with no privacy protection method. Therefore, the main advantages of this approach are fast speed, low computational loss based on privacy protection, optimised for the computational characteristics of financial scenarios, and the ability to provide different levels of protection for different usage scenarios.  
Although this paper depends on the communication efficiency of the usage scenario, this limitation is somewhat alleviated considering the prevalence of fiber bandwidth and 5G high-speed networks.  
In addition, although the model in this paper is tested with a large ImageNet dataset, how to deploy the model in this paper in a larger data volume environment and how to apply the model in this paper to a nonlinear computing scenario are the next research directions for the authors of this paper. At the same time, a large number of algorithms based on linear computing exist in the fields of smart agriculture, smart medicine, and remote sensing and mapping [48; 49; 50; 51; 52; 53; 54; 55; 56]. It will be the future work of the authors to migrate the privacy-preserving algorithms proposed in this paper to more application scenarios.  
Conceptualization, [PERSON] and J.G.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON]; writing--original draft preparation, [PERSON], [PERSON], X.Y. and J.G.; writing--review and editing, [PERSON], [PERSON], X.Y., J.G., X.L., Z.L. and J.R.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON] and J.R.; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China and Beijing Social Science Foundation, grant numbers 71873129 and 19 GLA002.  
The authors declare no conflict of interest.