Abstract
Sensitive personal information is at risk of exposure by the institutions it is shared. Institutions are responsible for preserving the privacy of the personal data they hold, even more so, in the case of sensitive data. This paper shows the design of ICIS, a model that considers the context to identify 55 personal data types in unstructured texts of government type documents, regardless the size and type, and then classify each text segment as sensitive personal information, using natural language processing and machine learning techniques. ICIS not only indicates whether a text segment contains sensitive information or not, it also indicates personal data identified in each text segment, their location in the document and whether each text segment is classified as sensitive information. The main contributions of this work are both the identification of personal data and the classification of sensitive information based on the context, and the definition of sensitive personal information, in computational terms.
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Acknowledgements
This work was supported by Consejo Nacional de Ciencia y Tecnología (CONACyT), Instituto Politécnico Nacional (IPN), Comisión de Operación y Fomento de Actividades Académicas del IPN (COFAA), Programa de Estímulos al Desempeño de los Investigadores del IPN (EDI), Secretaría de Investigación y Posgrado del IPN (SIP), Convenio IPN-OAG-100-2021, Organization of American States (OAS), Cisco and the Citi Foundation, thanks to projects SIP 20222092, SIP 20211758 and the project Plataforma de Identificación, Clasificación y Monitoreo de Información Sensible (PICIS), winner of the Innovation Fund for Cybersecurity Projects in Latin America and the Caribbean 2021, created by OAS, Cisco and Citi Foundation.
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De Jesus Sanchez, S., Anaya, E.A., Calvo, H., Torres, J.E.C., Bermejo, R.A. (2023). ICIS: A Model for Context-Based Classification of Sensitive Personal Information. In: Mata-Rivera, M.F., Zagal-Flores, R., Barria-Huidobro, C. (eds) Telematics and Computing. WITCOM 2023. Communications in Computer and Information Science, vol 1906. Springer, Cham. https://doi.org/10.1007/978-3-031-45316-8_28
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DOI: https://doi.org/10.1007/978-3-031-45316-8_28
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