Publications tagged with "Anonymization"

1. Sensitive Information Detection Adopting Named Entity Recognition: A Proposed Methodology

   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

   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.

   DOI Publisher Details Copy BibTeX Download .bib

   {"key"=>"Campanile2022377", "type"=>"Conference paper", "bibtex"=>"@article{Campanile2022377,\n  author = {Campanile, Lelio and de Biase, Maria Stella and Marrone, Stefano and Marulli, Fiammetta and Raimondo, Mariapia and Verde, Laura},\n  title = {Sensitive Information Detection Adopting Named Entity Recognition: A Proposed Methodology},\n  year = {2022},\n  journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n  volume = {13380 LNCS},\n  pages = {377 – 388},\n  doi = {10.1007/978-3-031-10542-5_26}\n}\n", "author"=>"Campanile, Lelio and de Biase, Maria Stella and Marrone, Stefano and Marulli, Fiammetta and Raimondo, Mariapia and Verde, Laura", "author_array"=>[{"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Maria Stella", "last"=>"Biase", "prefix"=>"de", "suffix"=>""}, {"first"=>"Stefano", "last"=>"Marrone", "prefix"=>"", "suffix"=>""}, {"first"=>"Fiammetta", "last"=>"Marulli", "prefix"=>"", "suffix"=>""}, {"first"=>"Mariapia", "last"=>"Raimondo", "prefix"=>"", "suffix"=>""}, {"first"=>"Laura", "last"=>"Verde", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Lelio", "author_0_last"=>"Campanile", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Maria Stella", "author_1_last"=>"Biase", "author_1_prefix"=>"de", "author_1_suffix"=>"", "author_2_first"=>"Stefano", "author_2_last"=>"Marrone", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Fiammetta", "author_3_last"=>"Marulli", "author_3_prefix"=>"", "author_3_suffix"=>"", "author_4_first"=>"Mariapia", "author_4_last"=>"Raimondo", "author_4_prefix"=>"", "author_4_suffix"=>"", "author_5_first"=>"Laura", "author_5_last"=>"Verde", "author_5_prefix"=>"", "author_5_suffix"=>"", "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", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135889610&doi=10.1007%2f978-3-031-10542-5_26&partnerID=40&md5=3f22e351a3b3a961fed6a8cbe9a61cba", "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.", "author_keywords"=>"Anonymization; Data privacy; Information extraction; Named entity recognition; Sensitive information", "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", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 4"}
       

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2. Dataset Anonimyzation for Machine Learning: An ISP Case Study

   Campanile, L., Forgione, F., Marulli, F., Palmiero, G., & Sanghez, C. (2021). Dataset Anonimyzation for Machine Learning: An ISP Case Study [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12950 LNCS, 589–597. https://doi.org/10.1007/978-3-030-86960-1_42

   Abstract

   Internet Service Providers technical support needs personal data to predict potential anomalies. In this paper, we performed a comparative study of forecasting performance using raw data and anonymized data, in order to assess how much performance may vary, when plain personal data are replaced by anonymized personal data. © 2021, Springer Nature Switzerland AG.

   DOI Publisher Details Copy BibTeX Download .bib

   {"key"=>"Campanile2021589", "type"=>"Conference paper", "bibtex"=>"@article{Campanile2021589,\n  author = {Campanile, Lelio and Forgione, Fabio and Marulli, Fiammetta and Palmiero, Gianfranco and Sanghez, Carlo},\n  title = {Dataset Anonimyzation for Machine Learning: An ISP Case Study},\n  year = {2021},\n  journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n  volume = {12950 LNCS},\n  pages = {589 – 597},\n  doi = {10.1007/978-3-030-86960-1_42}\n}\n", "author"=>"Campanile, Lelio and Forgione, Fabio and Marulli, Fiammetta and Palmiero, Gianfranco and Sanghez, Carlo", "author_array"=>[{"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Fabio", "last"=>"Forgione", "prefix"=>"", "suffix"=>""}, {"first"=>"Fiammetta", "last"=>"Marulli", "prefix"=>"", "suffix"=>""}, {"first"=>"Gianfranco", "last"=>"Palmiero", "prefix"=>"", "suffix"=>""}, {"first"=>"Carlo", "last"=>"Sanghez", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Lelio", "author_0_last"=>"Campanile", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Fabio", "author_1_last"=>"Forgione", "author_1_prefix"=>"", "author_1_suffix"=>"", "author_2_first"=>"Fiammetta", "author_2_last"=>"Marulli", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Gianfranco", "author_3_last"=>"Palmiero", "author_3_prefix"=>"", "author_3_suffix"=>"", "author_4_first"=>"Carlo", "author_4_last"=>"Sanghez", "author_4_prefix"=>"", "author_4_suffix"=>"", "title"=>"Dataset Anonimyzation for Machine Learning: An ISP Case Study", "year"=>"2021", "journal"=>"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)", "volume"=>"12950 LNCS", "pages"=>"589 – 597", "doi"=>"10.1007/978-3-030-86960-1_42", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125259327&doi=10.1007%2f978-3-030-86960-1_42&partnerID=40&md5=6d455ec0ee65b4ddced72d8d76f57eb6", "abstract"=>"Internet Service Providers technical support needs personal data to predict potential anomalies. In this paper, we performed a comparative study of forecasting performance using raw data and anonymized data, in order to assess how much performance may vary, when plain personal data are replaced by anonymized personal data. © 2021, Springer Nature Switzerland AG.", "author_keywords"=>"Attribute suppression; Character masking; Cryptography; Customer Premise Equipment; Data generalization; Hash; ISP; Logistic regression; Pseudo-anonymization; Random forest; WISP", "keywords"=>"Logistic regression; Machine learning; Anonymization; Attribute suppression; Character masking; Customer premises equipment; Data generalization; Hash; ISP; Pseudo-anonymization; Random forests; WISP; Decision trees", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 1"}
       

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