This Machine Learning Research Presents a Review on Advancing Differential Privacy in High-Dimensional Linear Models: Balancing Accuracy with Data Confidentiality

In data science, linear models such as linear and logistic regression have long been celebrated for their straightforwardness and efficacy in drawing meaningful inferences from data. These models excel in scenarios where the relationship between input variables and outcomes is linear, making them invaluable tools for predicting consumer demand, assessing medical risks, and identifying potential […]

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