Venue & metadata

• Journal/Proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Volume: 13380 LNCS
• Pages: 377 – 388
• Note: Cited by: 4
• Author keywords: Anonymization; Data privacy; Information extraction; Named entity recognition; Sensitive information

Abstract

Protecting and safeguarding privacy has become increasingly important, especially in recent years. The increasing possibilities of acquiring and sharing personal information and data through digital devices and platforms, such as apps or social networks, have increased the risks of privacy breaches. In order to effectively respect and guarantee the privacy and protection of sensitive information, it is necessary to develop mechanisms capable of providing such guarantees automatically and reliably. In this paper we propose a methodology able to automatically recognize sensitive data. A Named Entity Recognition was used to identify appropriate entities. An improvement in the recognition of these entities is achieved by evaluating the words contained in an appropriate context window by assessing their similarity to words in a domain taxonomy. This, in fact, makes it possible to refine the labels of the recognized categories using a generic Named Entity Recognition. A preliminary evaluation of the reliability of the proposed approach was performed. In detail, texts of juridical documents written in Italian were analyzed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Natural language processing systems GS Sensitive data GS Anonymization GS Context window GS Domain taxonomy GS Information detection GS Information extraction GS Named entity recognition GS Personal information GS Privacy breaches GS Sensitive datas GS Sensitive informations GS Digital devices GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Campanile, L., de Biase, M. S., Marrone, S., Marulli, F., Raimondo, M., & Verde, L. (2022). Sensitive Information Detection Adopting Named Entity Recognition: A Proposed Methodology [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13380 LNCS, 377–388. https://doi.org/10.1007/978-3-031-10542-5_26