The article discusses the balance between privacy protection and data utility in natural language processing (NLP) through the lens of Differential Privacy. It emphasizes the necessity of creating benchmarks for privacy-preserving data practices and introduces a novel Privacy-oriented Entity Recognizer. By analyzing datasets and various NLP approaches, the research highlights the key privacy risk indicators that need to be addressed to enhance data security. Moreover, it evaluates the implications of privacy-preserving methodologies on NLP model performance and provides insights for future research directions in safeguarding sensitive information.
The study analyzes privacy-preserving data publishing methods, specifically focusing on Differential Privacy and its implementations within NLP, aiming to balance data utility against privacy risks.
Our findings indicate that while Differential Privacy techniques can mitigate privacy risks, they may compromise model performance, especially in sensitive NLP tasks.
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