Regression Analysis With Differential Privacy Preserving

Abstract

In the field of data mining, protecting sensitive data from being leaked is part of the focuses of current research. As a strict and provable definition of privacy model, differential privacy provides an excellent solution to the problem of privacy leakage. Numerous methods have been suggested to enforce differential privacy in various data mining tasks, such as regression analysis. However, existing solutions for regression analysis is less than satisfactory since the amount of noise added is excessive. What's worse, the adversary can launch model inversion attacks to infer sensitive information with the published regression model. Motivated by this, we propose a differential privacy budget allocation model. We optimize the regression model by adjusting the privacy budget allocation within the objective function. Extensive evaluation results show the superiority of the proposed model in terms of noise reduction, model inversion attack proof, and the trade-off between privacy protection and data utility.

Keywords

• Machine learning
• differential privacy
• regression analysis
• model inversion attack