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Title:Detecting privacy preferences from online social footprints: a literature review
Author(s):Khazaei, Taraneh; Xiao, Lu; Mercer, Robert E.; Khan, Atif
Subject(s):Social Privacy
Privacy Preference
Personalization
Machine Learning
User Modeling
Abstract:Providing personalized content can be of great value to both users and vendors. However, effective personalization hinges on collecting large amounts of personal data about users. With the exponential growth of activities in social networking websites, they have become a prominent platform to gather and analyze such information. Even though there exist a considerable number of social media users with publicly available data, previous studies have revealed a dichotomy between privacy-related intentions and behaviours. Users often face difficulties specifying privacy policies that are consistent with their actual privacy concerns and attitudes, and simply follow the default permissive privacy setting. Therefore, despite the availability of data, it is imperative to develop and employ algorithms to automatically predict users’ privacy preferences for personalization purposes. In this document, we review prior studies that tackle this challenging task and make use of users’ online social footprints to discover their desired privacy settings.
Issue Date:2016-03-15
Publisher:iSchools
Citation Info:NA
Series/Report:IConference 2016 Proceedings
Genre:Conference Paper / Presentation
Type:Text
Language:English
URI:http://hdl.handle.net/2142/89298
DOI:10.9776/16293
Rights Information:Copyright 2016 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2016-03-08


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