In the era of digital intelligence,data is a key element in promoting social and economic development.Educational data,as a vital component of data,not only supports teaching and learning but also contains much sensit...In the era of digital intelligence,data is a key element in promoting social and economic development.Educational data,as a vital component of data,not only supports teaching and learning but also contains much sensitive information.How to effectively categorize and protect sensitive data has become an urgent issue in educational data security.This paper systematically researches and constructs a multi-dimensional classification framework for sensitive educational data,and discusses its security protection strategy from the aspects of identification and desensitization,aiming to provide new ideas for the security management of sensitive educational data and to help the construction of an educational data security ecosystem in the era of digital intelligence.展开更多
Artificial intelligence has been widely used in the financial field, such as credit risk assessment, fraud detection, and stock prediction. Training deep learning models requires a significant amount of data, but fina...Artificial intelligence has been widely used in the financial field, such as credit risk assessment, fraud detection, and stock prediction. Training deep learning models requires a significant amount of data, but financial data often contains sensitive information, some of which cannot be disclosed. Acquiring large amounts of financial data for training deep learning models is a pressing issue that needs to be addressed. This paper proposes a Noise Visibility Function-Differential Privacy Generative Adversarial Network (NVF-DPGAN) model, which generates privacy preserving data similar to the original data, and can be applied to data augmentation for deep learning. This study conducts experiments using financial data from China Stock Market & Accounting Research (CSMAR) database. It compares the generated data with real data from various perspectives, including mean, probability density distribution, and correlation. The experimental results show that the two datasets exhibit similar characteristics. A time series forecasting model is trained on the generated data and the real data separately, and their prediction results are closely aligned. NVF-DPGAN model is feasible and practical in terms of financial data enhancement and privacy protection. This method can also be generalized to other fields, such as the privacy protection of medical data.展开更多
基金Education Science planning project of Jiangsu Province in 2024(Grant No:B-b/2024/01/152)2025 Jiangsu Normal University Graduate Research and Innovation Program school-level project“Research on the Construction and Desensitization Strategies of Education Sensitive Data Classification from the Perspective of Educational Ecology”。
文摘In the era of digital intelligence,data is a key element in promoting social and economic development.Educational data,as a vital component of data,not only supports teaching and learning but also contains much sensitive information.How to effectively categorize and protect sensitive data has become an urgent issue in educational data security.This paper systematically researches and constructs a multi-dimensional classification framework for sensitive educational data,and discusses its security protection strategy from the aspects of identification and desensitization,aiming to provide new ideas for the security management of sensitive educational data and to help the construction of an educational data security ecosystem in the era of digital intelligence.
文摘Artificial intelligence has been widely used in the financial field, such as credit risk assessment, fraud detection, and stock prediction. Training deep learning models requires a significant amount of data, but financial data often contains sensitive information, some of which cannot be disclosed. Acquiring large amounts of financial data for training deep learning models is a pressing issue that needs to be addressed. This paper proposes a Noise Visibility Function-Differential Privacy Generative Adversarial Network (NVF-DPGAN) model, which generates privacy preserving data similar to the original data, and can be applied to data augmentation for deep learning. This study conducts experiments using financial data from China Stock Market & Accounting Research (CSMAR) database. It compares the generated data with real data from various perspectives, including mean, probability density distribution, and correlation. The experimental results show that the two datasets exhibit similar characteristics. A time series forecasting model is trained on the generated data and the real data separately, and their prediction results are closely aligned. NVF-DPGAN model is feasible and practical in terms of financial data enhancement and privacy protection. This method can also be generalized to other fields, such as the privacy protection of medical data.
