Privacy Information Classification: A Hybrid Approach
This work addresses privacy protection for online social network users, but it appears incremental as it combines existing methods without introducing a fundamentally new paradigm.
The study tackled the problem of privacy leakage in online social networks by proposing a hybrid approach combining deep learning and ontology-based models to detect and classify privacy-related information, with empirical results demonstrating its effectiveness in assisting users.
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.