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Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-02242020-153034


Tipo di tesi
Tesi di dottorato di ricerca
Autore
PELLUNGRINI, ROBERTO
URN
etd-02242020-153034
Titolo
Modeling & Predicting Privacy Risk in Personal Data
Settore scientifico disciplinare
INF/01
Corso di studi
INFORMATICA
Relatori
tutor Prof.ssa Monreale, Anna
supervisore Prof. Pedreschi, Dino
Parole chiave
  • privacy risk assessment
  • privacy
  • privacy risk prediction
  • human mobility data
  • retail data
Data inizio appello
04/03/2020
Consultabilità
Completa
Riassunto
Privacy in Big Data analytics is one of the most important issues that analysts and businesses face when managing personal data. In a privacy preserving analysis process, the privacy risk on the individuals represented in the data is firstly evaluated, then the data is appropriately modified in order to preserve privacy while at the same time maintaining a certain level of data quality. In this thesis we focus on privacy risk assessment, proposing new models and algorithms to deal with this fundamental part of privacy aware systems. We propose some extensions to an existing state-of-the-art privacy risk assessment framework, to improve on existing literature. Then, we propose a classification based methodology to predict privacy risk. We validate our proposal on three different types of real world data: human mobility, retail and social network data. Finally we propose a new model for the behavior of an adversary in human mobility data, leveraging the natural structure and constraints of this kind of data.
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