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"}