This article describes the use of advanced NLP techniques such as fine-tuned RoBERTa models to evaluate and mitigate re-identification risks in privacy-preserving data publishing. It emphasizes the need for well-curated, labeled training data for developing effective sequence labeling models, which assign tokens to MASK or NO MASK categories. The study highlights various privacy risk indicators and presents quantitative assessments through experimentation, intending to enhance data privacy while enabling practical applications of anonymized datasets.
The study employs a fine-tuned RoBERTa model to evaluate re-identification risks in data anonymization, focusing on the importance of labeled training data.
By implementing a sequence labelling model, we can assess re-identification risks, ultimately enhancing privacy in data publishing while considering various masking strategies.
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