  A large amount of information has been published to online social networks
every day. Individual privacy-related information is also possibly disclosed
unconsciously by the end-users. Identifying privacy-related data and protecting
the online social network users from privacy leakage turn out to be
significant. Under such a motivation, this study aims to propose and develop a
hybrid privacy classification approach to detect and classify privacy
information from OSNs. The proposed hybrid approach employs both deep learning
models and ontology-based models for privacy-related information extraction.
Extensive experiments are conducted to validate the proposed hybrid approach,
and the empirical results demonstrate its superiority in assisting online
social network users against privacy leakage.
