Giovanni Camarda
Design and Implementation of a privacy-preserving framework for Machine Learning.
Rel. Marco Mellia, Martino Trevisan, Nikhil Jha. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021
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Abstract
During the last decade, a myriad of new technologies has changed the way society perceives everyday life, embodying the Big Data Era peculiarities. Almost every technological scenario produces an incredible amount of data, from disparate physical sources and at a very different generation rate, creating an interconnected and interdependent network of people and data. For this reason, data has become for companies and organizations a strategical asset to drive businesses, to tailor user-specific services and to obtain a more relevant position on data markets. More and more companies collect and process customers personal data requiring it in exchange for services, forcing users to accept a power unbalanced transaction.
To tackle this situation, regulations as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) were signed in 2018 and 2020, enforcing data protection respectively in the European Union and California State: their primary goal consists in support free data flow, building trust conditions and rebalancing powers in the relationship between companies and customers
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