Privacy Preserving for NLP Using Differential Privacy

bstract - One of the most popular frameworks for guaranteeing data privacy is differential privacy preserving statistical utility. However, its practical application faces critical challenges, including the lack of a standardized approach for selecting privacy parameters, limitations in flexibility for diverse real world scenarios, and vulnerabilities in data-dependent settings. This paper offers a unique project that tackles these issues by using an enhanced differential privacy mechanism tailored for realworld datasets. Our research introduces an adaptive method for dynamically selecting the privacy parameter (ε), maintaining the best possible balance between data utility and privacy protection. Additionally, we enhance differential privacy mechanisms to support broader applications by customizing noise injection techniques, making them more adaptable to various data types and use cases.