An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation

Abstract

With the support of cloud computing technology, it is easier for financial institutions to obtain more key information about the whole industry chain. However, the massive use of financial data has many potential risks. In order to better cope with this dilemma and better protect the financial privacy of users, we propose a privacy protection model based on cloud computing. The model provides four levels of privacy protection according to the actual needs of users. At the highest level of protection, the server could not access any information about the user and the raw data, nor could it recover the computational characteristics of the data. In addition, due to the universality of the mathematical principle of linear operators, the model could effectively protect and accelerate all models based on linear operations. 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. Finally, we design a multi-user scheduling model to deploy the model in a real scenario, which could maximise server power and protect user privacy as well.

Integrating Merkle Trees with Transformer Networks for Secure Financial Computation

“…When selecting datasets, attention is paid to the diversity, authenticity, and completeness of the data. Prominent public financial transaction datasets include the Credit Card Fraud Detection dataset provided by Kaggle [29]. The reason for utilizing these datasets is that they offer a wealth of real-world cases, assisting in effectively testing and optimizing the performance of the Merkle-Transformer model in financial attack detection.…”

“…These datasets are typically used for training and testing stock price prediction models, to forecast future stock market trends. The stock price data for this study are obtained from various public financial market databases, including Yahoo Finance, Google Finance, and others [29]. Additionally, professional financial data service providers like Bloomberg and Reuters offer high-quality stock price data.…”

Navigating the Data Security Landscape: Challenges and Solutions in Financial Markets amid Digitalization and Artificial Intelligence

“…At a microscopic level, mishandling of personal details, or disclosure of organizational information constitutes the personal information and disrupts the systematic processes of the financial markets. In case of severe circumstances, such activities impose risk on financial security with regards to a specific organization, that may threaten the economy as a whole in certain situations [9]. Similarly, encroachment of personal details and disruption of law by certain organizations make it difficult for individuals to assess the challenges or problems encountered within financial markets.…”

Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

“…By adopting a decentralized and distributed approach to storing transaction data, blockchain serves as a robust deterrent against data tampering and unauthorized access. This innovative application of technology empowers financial institutions to securely share and verify data, all while ensuring comprehensive protection of customer privacy [8,9].…”