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Deep learning application – Identifying PII (personally identifiable information) to protect

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  • Anil K. Makhija

    (CamEd Business School, Cambodia)

Abstract

This paper presents application of deep learning and machine learning models in detecting personally identifiable information (PII) in unstructured text (emails). The proposed models use support vector machine (trained using sequential minimal optimization) and long short term memory (LSTM) artificial neural network. Synthetic email dataset has been used to train and validate the proposed models and the outcomes are measured by standard measures of accuracy, precision, recall and F1-score of each of the proposed model. The experimental results on the model that uses support vector machine (trained using sequential minimal optimization) showed most promising results on detecting the personally identifiable information in the email dataset. The LSTM model also showed equally promising results.

Suggested Citation

  • Anil K. Makhija, 2020. "Deep learning application – Identifying PII (personally identifiable information) to protect," Journal of Accounting, Finance, Economics, and Social Sciences, CamEd Business School, vol. 5(2), pages 10-16, December.
    • Handle: RePEc:euj:jafess:v:5:y:2020:i:2:id:23
    DOI: 10.62458/jafess.160224.5(2)10-16
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