Identity validation of Online Social Networks' (OSNs') peers is a critical concern to the insurance of safe and secure online socializing environments. Starting from the vision of empowering users to determine the validity of OSN identities, we suggest a framework to estimate the trustworthiness of online social profiles based only on the information they contain. Our framework is based on learning identity correlations between profile attributes in an OSN community and on collecting ratings from OSN community members to evaluate the trustworthiness of target profiles. Our system guarantees utility, user anonymity, impartiality in rating, and operability within the dynamics and continuous evolution of OSNs. In this article, we detail the system design, and we prove its correctness against these claimed quality properties. Moreover, we test its effectiveness, feasibility, and efficiency through experimentation on real-world datasets from Facebook and Google+, in addition to using the Adults UCI dataset.

COIP—Continuous, Operable, Impartial, and Privacy-Aware Identity Validity Estimation for OSN Profiles

CARMINATI, BARBARA;FERRARI, ELENA
2016-01-01

Abstract

Identity validation of Online Social Networks' (OSNs') peers is a critical concern to the insurance of safe and secure online socializing environments. Starting from the vision of empowering users to determine the validity of OSN identities, we suggest a framework to estimate the trustworthiness of online social profiles based only on the information they contain. Our framework is based on learning identity correlations between profile attributes in an OSN community and on collecting ratings from OSN community members to evaluate the trustworthiness of target profiles. Our system guarantees utility, user anonymity, impartiality in rating, and operability within the dynamics and continuous evolution of OSNs. In this article, we detail the system design, and we prove its correctness against these claimed quality properties. Moreover, we test its effectiveness, feasibility, and efficiency through experimentation on real-world datasets from Facebook and Google+, in addition to using the Adults UCI dataset.
2016
http://dl.acm.org/citation.cfm?id=3014338
Community-sourcing; Identity validation; Online social networks; Privacy preservation; Computer Networks and Communications
Leila, Bahri; Carminati, Barbara; Ferrari, Elena
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2061790
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