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A Semi-supervised Approach to Measuring User Privacy in Online Social Networks

Contributo in Atti di convegno
Data di Pubblicazione:
2016
Abstract:
During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user’s direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.
Tipologia CRIS:
04A-Conference paper in volume
Keywords:
privacy metrics, active learning, online social networks
Elenco autori:
Pensa, R.G.; Di Blasi, G.
Autori di Ateneo:
PENSA Ruggero Gaetano
Link alla scheda completa:
https://iris.unito.it/handle/2318/1600991
Link al Full Text:
https://iris.unito.it/retrieve/handle/2318/1600991/234271/privacysocial_ds16_4aperto.pdf
Titolo del libro:
Discovery Science, Proceedings of the 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016
Pubblicato in:
LECTURE NOTES IN COMPUTER SCIENCE
Journal
LECTURE NOTES IN COMPUTER SCIENCE
Series
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URL

http://link.springer.com/chapter/10.1007/978-3-319-46307-0_25
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