Litoiu, MarinHafez, Mohamed Mohamed Nader2026-07-242026-07-242026-04-232026-07-24https://hdl.handle.net/10315/43932The robust extraction of Personally Identifiable Information (PII) is essential for privacy protection in modern text-processing systems, where users often share sensitive details in prompts, emails, chat logs, and support tickets. As PII categories and deployment domains evolve, updating extraction models can improve coverage but may also cause catastrophic forgetting of previously learned types. This thesis investigates how PII extraction can remain accurate, reliable, and maintainable as task scope expands. It introduces ASC-PIE, a unified English corpus and evaluation framework that combines public datasets with a synthetic component to improve coverage of rare and challenging PII cases without using real personal data. Using ASC-PIE, the thesis compares encoder-based, encoder-decoder, and decoder-only models under supervised fine-tuning and in-context prompting. It also proposes SPRINT-PP, a privacy-safe continual-learning method that mitigates forgetting without storing raw historical examples. The evaluation covers extraction quality, robustness, output validity, computational efficiency, and knowledge retention.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Information technologyComputer scienceComputer engineeringElectronic Thesis or Dissertation2026-07-24Personally identifiable informationPII extractionNamed entity recognitionPrivacy protectionLarge language modelsIn-context promptingSupervised fine-tuningContinual learningCatastrophic forgettingSynthetic datasets