Social Privacy

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. Give the exponential growth in online activities in social networking sites; they can be a great platform to gather and analyze such information. In spite of the considerable number of user profiles with publicly available data, previous studies have shown that social media users often face difficulties in specifying the privacy policies that are consistent with their privacy concerns and attitudes. Therefore, even when the data is available, it is necessary to employ other techniques to predict users privacy preferences. In this project, we aim to make use of users social profiles and activities to build predictive models and to automatically discover their desired privacy settings for purpose of personalization and direct marketing.

Faculty Supervisor:

Lu Xiao

Student:

Taraneh Khazaei

Partner:

InfoTrellis

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Western University