Privacy in text documents

Abstract

The process of sensitive data preservation is a manual and a semi-automatic procedure. Sensitive data preservation suffers various problems, in particular, affect the handling of confidential, sensitive and personal information, such as the identification of sensitive data in documents requiring human intervention that is costly and propense to generate error, and the identification of sensitive data in large-scale documents does not allow an approach that depends on human expertise for their identification and relationship. DataSense will be highly exportable software that will enable organizations to identify and understand the sensitive data in their possession in unstructured textual information (digital documents) in order to comply with legal, compliance and security purposes. The goal is to identify and classify sensitive data (Personal Data) present in large-scale structured and non-structured information in a way that allows entities and/or organizations to understand it without calling into question security or confidentiality issues. The DataSense project will be based on European-Portuguese text documents with different approaches of NLP (Natural Language Processing) technologies and the advances in machine learning, such as Named Entity Recognition, Disambiguation, Co-referencing (ARE) and Automatic Learning and Human Feedback. It will also be characterized by the ability to assist organizations in complying with standards such as the GDPR (General Data Protection Regulation), which regulate data protection in the European Union.