Catelli, Rosario (2021) Safeguarding Privacy Through Deep Learning Techniques. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Resource language: English
Title: Safeguarding Privacy Through Deep Learning Techniques
Creators:
CreatorsEmail
Catelli, Rosariorosario.catelli@unina.it
Date: 13 April 2021
Number of Pages: 125
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Elettrica e delle Tecnologie dell'Informazione
Dottorato: Information technology and electrical engineering
Ciclo di dottorato: 33
Coordinatore del Corso di dottorato:
nomeemail
Riccio, Danieledaniele.riccio@unina.it
Tutor:
nomeemail
Casola, ValentinaUNSPECIFIED
Esposito, MassimoUNSPECIFIED
Date: 13 April 2021
Number of Pages: 125
Keywords: privacy, deep learning, clinical de-identification
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni
Additional information: Google Scholar Profile for updated contact email: https://scholar.google.it/citations?user=Ge-RyG4AAAAJ&hl=it&oi=ao - OrcID Profile: https://orcid.org/0000-0001-5598-6477
Date Deposited: 10 May 2021 23:23
Last Modified: 07 Jun 2023 10:34
URI: http://www.fedoa.unina.it/id/eprint/13880

Collection description

Over the last few years, there has been a growing need to meet minimum security and privacy requirements. Both public and private companies have had to comply with increasingly stringent standards, such as the ISO 27000 family of standards, or the various laws governing the management of personal data. The huge amount of data to be managed has required a huge effort from the employees who, in the absence of automatic techniques, have had to work tirelessly to achieve the certification objectives. Unfortunately, due to the delicate information contained in the documentation relating to these problems, it is difficult if not impossible to obtain material for research and study purposes on which to experiment new ideas and techniques aimed at automating processes, perhaps exploiting what is in ferment in the scientific community and linked to the fields of ontologies and artificial intelligence for data management. In order to bypass this problem, it was decided to examine data related to the medical world, which, especially for important reasons related to the health of individuals, have gradually become more and more freely accessible over time, without affecting the generality of the proposed methods, which can be reapplied to the most diverse fields in which there is a need to manage privacy-sensitive information.

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