Privacy as a service in social network communications
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conference contribution
posted on 2025-01-15, 01:13authored byBS Vidyalakshmi, RK Wong, M Ghanavati, C Chi
With dispersing of information on social networks - both personally identifiable and general - comes the risk of these information falling into wrong hands. Users are burdened with setting privacy of multiple social networks, each with growing number of privacy settings. Exponential growth of applications (App) running on social networks have made privacy control increasingly difficult. This necessitates Privacy as a service model, especially for social networks, to handle privacy across multiple applications and platforms. Privacy aware information dispersal involves knowing who is receiving what information of ours. Our proposed service employs a supervised learning model to assist user in spotting unintended audience for a post. Different from previous work, we combine both Tie-strength and Context of the information as features in learning. Our evaluation using several classification techniques shows that the proposed method is effective and better than methods using either only Tie-strength or only Context of the information for classification.
History
Publication title
Proceedings of 2014 IEEE International Conference on Services Computing
Volume
2
Editors
E Ferrari, R Kaliappa, P Hung
Pagination
456-463
ISBN
978-1-4799-5066-9
Department/School
Information and Communication Technology
Publisher
IEEE
Publication status
Published
Place of publication
Piscataway, United States
Event title
2014 IEEE International Conference on Services Computing
Event Venue
Anchorage, Alaska
Date of Event (Start Date)
2014-06-27
Date of Event (End Date)
2014-07-02
Rights statement
Copyright 2014 IEEE
Socio-economic Objectives
280115 Expanding knowledge in the information and computing sciences