Publication Date
2014
Document Type
Thesis
Committee Members
Keke Chen (Advisor), Guozhu Dong (Committee Member), Krishnaprasad Thirunarayan (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Privacy has been a big concern for users of social network services (SNS). On recent criticism about privacy protection, most SNS now provide fine privacy controls, allowing users to set visibility levels for almost every profile item. However, this also creates a number of difficulties for users. First, SNS providers often set most items by default to the highest visibility to improve the utility of social network, which may conflict with users' intention. It is often formidable for a user to fine-tune tens of privacy settings towards the user desired settings. Second, tuning privacy settings involves an intricate tradeoff between privacy and utility. When you turn off the visibility of one item to protect your privacy, the social utility of that item is turned off as well. It is challenging for users to make a tradeoff between privacy and utility for each privacy setting. We propose a framework for users to conveniently tune the privacy settings towards the user desired privacy level and social utilities. It mines the privacy settings of a large number of users in a SNS, e.g., Facebook, to generate latent trait models for the level of privacy concern and the level of utility preference. A tradeoff algorithm is developed for helping users find the optimal privacy settings for a specified level of privacy concern and a personalized utility preference. We crawl a large number of Facebook accounts and derive the privacy settings with a novel method. These privacy setting data are used to validate and showcase the proposed approach.
Page Count
52
Department or Program
Department of Computer Science
Year Degree Awarded
2014
Copyright
Copyright 2014, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may be modified only if the modified version is distributed with these same permissions. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.
