| dc.description.abstract | Nowadays, thanks to the digitalization of business processes and public
administrations, many significant Big data collections are available. Users are direct
suppliers of data when publishing contents on social networks. However, when using
a service on the web, users must often provide their data, which will become property
of the company running the service. To this end, users need to be aware of the privacy
issues related to the management of their data, whereas companies need to ensure
the protection of users' personal data, also according to new laws and regulations
issued by governments. On the other hand, there exists the necessity not to limit the
processing of data by companies and other public institutions. Thus, it is necessary to
devise methods devoted to the identification of possible privacy threats during
users' online activities, and to develop privatization strategies that possibly do not
downgrade the significance of data.
This dissertation provides experimental evidence of several threats for users when
providing their personal data for accessing online services, aiming to increase their
awareness, and it describes new methodologies and tools to support companies
when processing personal data of their users. In particular, the proposed
methodologies exploit data correlations expressed in terms of relaxed functional
dependencies (RFDs) to define privatization strategies, aiming to safeguard
user's privacy, and to detect malicious accounts in social networks. Finally, two
automatic tools have been designed and implemented to help users better
understand privacy threats during their online activities. [edited by Author] | it_IT |
