Deep Learning Application – Identifying PII (Personally Identifiable Information) to Protect

dc.creatorMakhija, Anil K.
dc.date2020-12-01
dc.date.accessioned2025-11-26T13:08:53Z
dc.descriptionThis 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.en-US
dc.formatapplication/pdf
dc.identifierhttps://jafess.com/index.php/home/article/view/23
dc.identifier10.62458/jafess.160224.5(2)10-16
dc.identifier.urihttps://cam-ed-oar.com/handle/123456789/8
dc.languageeng
dc.publisherCamEd Business Schoolen-US
dc.relationhttps://jafess.com/index.php/home/article/view/23/19
dc.rightshttps://creativecommons.org/licenses/by/4.0en-US
dc.sourceJournal of Accounting, Finance, Economics, and Social Sciences; Vol. 5 No. 2 (2020); 10-16en-US
dc.source2708-6178
dc.source2708-616X
dc.subjectPersonally Identifiable Informationen-US
dc.subjectDeep Learning in detecting PIIen-US
dc.subjectMachine Learning in detecting PIIen-US
dc.subjectArtificial Intelligence in protecting privacyen-US
dc.subjectProtecting Personally Identifiable Informationen-US
dc.titleDeep Learning Application – Identifying PII (Personally Identifiable Information) to Protecten-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

Files