York University · M.A. research

ASC-PIE: An Evaluation Framework for PII-Aware Named-Entity Recognition

I completed my M.A. in Information Systems & Technology at York University, and it was officially awarded in 2026.

ThesisOfficial degree status · 2026

SPRINT-PPSPRINT-PP is a research paper submitted and under review.

Research at a glance

A shared corpus, protocol, and reproducible benchmark

Total examples333,109

Entity mentions2,025,878

Canonical PII types19

Source datasets5

Supervised fine-tuning · strict F1

RoBERTa-large99.1%

FLAN-T5-base98.7%

ModernBERT-large84.6%

BERT-base-cased80.7%

Llama 3.1 8B69%

Qwen 2.5 7B40%

Continual-learning accuracy by strategy

SPRINT-PP83.5%

Distillation83%

Replay76.5%

Baseline14.6%

SPRINT-PP achieved the strongest tested accuracy while remaining privacy-safe and storing no raw historical PII.

Research framework

Privacy datasets move through preprocessing and shared PII labels before model training and named-entity recognition evaluation.

ASC-PIE evaluates named-entity recognition for personally identifiable information across privacy-focused datasets.

The ASC-PIE experiments prepare real and synthetic privacy datasets for NER training and evaluation.

The research pipeline maps differing PII label schemes into a shared representation for comparison.

ASC-PIE experiments use Python, PyTorch, Hugging Face Transformers, scikit-learn, and seqeval.

Research links