Abstract
Privacy issues of social media are getting tricky due to the increasing volume of social media users sharing through online social networks (OSNs). Existing privacy policy mechanisms of OSNs may not protect personal privacy effectively since users are struggle to set up the privacy settings. In this paper, we propose a privacy policy prediction model to help users to specify privacy policies for their textual posts. We investigate the semantic of posts, social context, and keywords associated with users’ privacy preferences as possible indicators of decision making, and build a multi-class classifier based on their historical posts and decisions. During the cold-start periods, the proposed model integrates crowdsourcing and machine learning to recommend privacy policies for new users. Experimental results shows that the overall match rate for all the data with random forest classifier is over 70%, with more than 50% correct prediction rate for new users.
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Notes
- 1.
Chinese Open Wordnet http://compling.hss.ntu.edu.sg/cow/.
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Acknowledgments
This work is supported by the National Key R&D Plan of China under grant no. 2016YFB0800201, the Natural Science Foundation of China under grant no. 61070212 and 61572165, the State Key Program of Zhejiang Province Natural Science Foundation of China under grant no. LZ15F020003, the Key research and development plan project of Zhejiang Province under grant no. 2017C01065, the Key Lab of Information Network Security, Ministry of Public Security, under grant no C16603.
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Chen, L. et al. (2018). A Privacy Settings Prediction Model for Textual Posts on Social Networks. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_53
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DOI: https://doi.org/10.1007/978-3-030-00916-8_53
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