Design of privacy preserving approaches in the Internet based on Machine Learning and Big Data
Reference persons MARCO MELLIA
External reference persons Martino Trevisan - Centro Interdipartimentale SmartData@PoliTO
Thesis type DATA ANALYSIS, DESIGN AND EXPERIMENTS
Description Every time we browse the web, use our smartphone or credit card, pieces of informations are collected by large companies that build a profile of us, understand our interests and use this to provide personalised content, such as targeted advertisements, or special discounts. This is just the top part of the iceberg, with a pletora of smaller companies that collect, process and trade informations they extracted from our browsing history.
In the context of European Project PIMCity, we aim at giving back end-users the right to identify which information to expose, to which company, for which usage.
In this thesis, we aim at creating a "privacy score", an are easy means to let end-users understand which data internet services are collecting, and their value. For this, we will leverage big data approaches, based on machine learning and graph analysis to automatically assign a privacy score to services.
Students will work in the SmartData@PoliTO center, and they will be involved in the PIMCity project, working in an international context, possibly also spending some time at some partners' places around Europe. It would be possible also to continue the work after graduation by joining a PhD.
Deadline 08/10/2020 PROPONI LA TUA CANDIDATURA