Privacy Information Classification: A Hybrid Approach

• URL: http://arxiv.org/abs/2101.11574v1
• Date: Wed, 27 Jan 2021 18:03:18 GMT
• Title: Privacy Information Classification: A Hybrid Approach
• Authors: Jiaqi Wu, Weihua Li, Quan Bai, Takayuki Ito, Ahmed Moustafa
• Abstract summary: This study proposes and develops 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.
• Score: 9.642559585173517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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