Evaluating Differential Privacy in Machine Learning Models: Methods, Applications, and Challenges

Abstract

Differential privacy has become a pivotal concept in ensuring the privacy and security of data used in machine learning models. This paper explores the methods, applications, and challenges associated with implementing differential privacy in machine learning. Differential privacy aims to provide robust privacy guarantees by ensuring that the removal or addition of a single data point does not significantly affect the output of a query, thereby protecting individual data entries from being inferred. We examine various techniques for incorporating differential privacy into machine learning, such as the Laplace mechanism, the Gaussian mechanism, and differential privacy in stochastic gradient descent. Additionally, we discuss the applications of differential privacy across different domains, including healthcare, finance, and social networks, highlighting its role in enabling the safe use of sensitive data. The paper also addresses the inherent challenges and trade-offs involved in applying differential privacy, such as the balance between privacy and model accuracy, the computational overhead, and the complexities of tuning privacy parameters. Through a comprehensive analysis, we aim to provide insights into the current state of differential privacy in machine learning and outline future directions for research and development in this critical area.