Bayesian networks based on differential privacy for financial data privacy

As the demand for privacy protection continues to grow, ensuring effective financial data processing while preventing sensitive information leakage has become a pressing issue in the financial industry and related research fields. We propose a financial data processing method based on Bayesian networks and differential privacy, innovatively introducing an exponential mechanism to optimize the Bayesian network and adding Laplace noise to disrupt the data, thereby reducing the risk of sensitive information leakage. Through the evaluation of data usability using support vector machines, experimental results show that the processed synthetic data is highly similar to the original data in key metrics, demonstrating that this method maintains data usability while protecting privacy.