Publications tagged with "WISP"

1. Evaluating the Impact of Data Anonymization in a Machine Learning Application

   Campanile, L., Forgione, F., Mastroianni, M., Palmiero, G., & Sanghez, C. (2022). Evaluating the Impact of Data Anonymization in a Machine Learning Application [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13380 LNCS, 389–400. https://doi.org/10.1007/978-3-031-10542-5_27

   Abstract

   The data protection impact assessment is used to verify the necessity, proportionality and risks of data processing. Our work is based on the data processed by the technical support of a Wireless Service Provider. The team of WISP tech support uses a machine learning system to predict failures. The goal of our the experiments was to evaluate the DPIA with personal data and without personal data. In fact, in a first scenario, the experiments were conducted using a machine learning application powered by non-anonymous personal data. Instead in the second scenario, the data was anonymized before feeding the machine learning system. In this article we evaluate how much the Data Protection Impact Assessment changes when moving from a scenario with raw data to a scenario with anonymized data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

   DOI Publisher Details Copy BibTeX Download .bib

   {"key"=>"Campanile2022389", "type"=>"Conference paper", "bibtex"=>"@article{Campanile2022389,\n  author = {Campanile, Lelio and Forgione, Fabio and Mastroianni, Michele and Palmiero, Gianfranco and Sanghez, Carlo},\n  title = {Evaluating the Impact of Data Anonymization in a Machine Learning Application},\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 = {389 – 400},\n  doi = {10.1007/978-3-031-10542-5_27}\n}\n", "author"=>"Campanile, Lelio and Forgione, Fabio and Mastroianni, Michele and Palmiero, Gianfranco and Sanghez, Carlo", "author_array"=>[{"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Fabio", "last"=>"Forgione", "prefix"=>"", "suffix"=>""}, {"first"=>"Michele", "last"=>"Mastroianni", "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"=>"Michele", "author_2_last"=>"Mastroianni", "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"=>"Evaluating the Impact of Data Anonymization in a Machine Learning Application", "year"=>"2022", "journal"=>"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)", "volume"=>"13380 LNCS", "pages"=>"389 – 400", "doi"=>"10.1007/978-3-031-10542-5_27", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135904907&doi=10.1007%2f978-3-031-10542-5_27&partnerID=40&md5=b550cf760c368d94894bd31b11163284", "abstract"=>"The data protection impact assessment is used to verify the necessity, proportionality and risks of data processing. Our work is based on the data processed by the technical support of a Wireless Service Provider. The team of WISP tech support uses a machine learning system to predict failures. The goal of our the experiments was to evaluate the DPIA with personal data and without personal data. In fact, in a first scenario, the experiments were conducted using a machine learning application powered by non-anonymous personal data. Instead in the second scenario, the data was anonymized before feeding the machine learning system. In this article we evaluate how much the Data Protection Impact Assessment changes when moving from a scenario with raw data to a scenario with anonymized data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.", "author_keywords"=>"Data disappearance; DPIA; GDPR; Illegitimate access to data; Privacy; Risks; Unwanted modification of data; WISP", "keywords"=>"Computer aided instruction; Machine learning; Risk assessment; Data disappearance; Data protection impact assessments; DPIA; GDPR; Illegitimate access to data; Machine learning applications; Machine learning systems; Privacy; Unwanted modification of data; WISP; Data privacy", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 1"}
       

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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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