PII Data Detection using Keras & NLP

The research presents a reliable and effective method for automatically identifying and categorizing PII items in various textual datasets using advanced deep learning models, specifically KerasNLP, DeBERTaV3, and DistilBERT. Sensitive information including names, addresses, and social security numbers are included in personally identifiable information. The research makes use of neural network technology to automatically recognize and categorize PII elements found in textual data. A comprehensive method that includes training strategies specifically designed for PII detection, model architecture design, and preprocessing stages. The proposed model has demonstrated its ability to accurately identify Personally Identifiable Information (PII) by achieving an accuracy of 89.8% and a minimal loss of 0.0004% when adjusting to longer input sequences.

Authors

Publication Details

Published
2024-11-21
DOI
https://doi.org/10.1109/delcon64804.2024.10866085
Citations
2
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
1.42
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PII Data Detection using Keras & NLP

Perla Sree Neha, Beebi Naseeba, Yanni Rohan Kommathoti, Samudra Pabbathi
2 citations
Artificial Intelligence in Healthcare
1.42
article

PII Data Detection using Keras & NLP

Perla Sree Neha, Beebi Naseeba, Yanni Rohan Kommathoti, Samudra Pabbathi
article en
2 citations

Abstract

The research presents a reliable and effective method for automatically identifying and categorizing PII items in various textual datasets using advanced deep learning models, specifically KerasNLP, DeBERTaV3, and DistilBERT. Sensitive information including names, addresses, and social security numbers are included in personally identifiable information. The research makes use of neural network technology to automatically recognize and categorize PII elements found in textual data. A comprehensive method that includes training strategies specifically designed for PII detection, model architecture design, and preprocessing stages. The proposed model has demonstrated its ability to accurately identify Personally Identifiable Information (PII) by achieving an accuracy of 89.8% and a minimal loss of 0.0004% when adjusting to longer input sequences.

Openalex Percentile: Top 12%
Artificial Intelligence in Healthcare
1.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.