Sensitive Information Detection Adopting Named Entity Recognition: A Proposed Methodology

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Conference Lelio Campanile, Maria Stella Biase, Stefano Marrone, Fiammetta Marulli, Mariapia Raimondo, Laura Verde — 2022 · Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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

Links & artifacts

DOI Publisher

@article{Campanile2022377,
  author = {Campanile, Lelio and de Biase, Maria Stella and Marrone, Stefano and Marulli, Fiammetta and Raimondo, Mariapia and Verde, Laura},
  title = {Sensitive Information Detection Adopting Named Entity Recognition: A Proposed Methodology},
  year = {2022},
  journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  volume = {13380 LNCS},
  pages = {377 – 388},
  doi = {10.1007/978-3-031-10542-5_26}
}

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

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