Project Abstract
Online social networks, such as Facebook, Twitter, and Google+, have
become extremely popular. They have significantly changed our behaviors
for sharing information and socializing, especially among the younger
generation. However, the extreme popularity of such online social
networks has become a double-edged sword -- while promoting online
socialization, these systems also raise privacy issues. To protect user
privacy without compromising socialization functions, this project
articulates a unifying framework that bridges the gap between the
human-oriented and technology-centered perspectives. In particular, this
project is developing methods to (1) detect the discrepancies between
users' information sharing expectations and actual information
disclosure; (2) design a user-centered yet computationally-efficient
formal model of user privacy in social networks; and (3) develop a
mechanism to effectively enforce privacy policies in the proposed model.
The potential long-term social benefits are significant, since such
awareness may gradually change people's privacy perceptions and affect
their behavior in privacy-centric scenarios.
This project develops a concept of "Social Circles" to model social network access within a Restricted Access and Limited Control framework. Methods are being developed to derive social circles from a variety of types of existing information within the social network; these are used to determine appropriate access control settings. The project is assessing information flow and risk of leakage given such settings, including the issues raised by heterogeneity of systems. In addition to theoretical analysis of potential information flows with respect to a variety of adversary models, the project is conducting user studies to determine if this approach reduces the gap between perceived and actual privacy.
This project develops a concept of "Social Circles" to model social network access within a Restricted Access and Limited Control framework. Methods are being developed to derive social circles from a variety of types of existing information within the social network; these are used to determine appropriate access control settings. The project is assessing information flow and risk of leakage given such settings, including the issues raised by heterogeneity of systems. In addition to theoretical analysis of potential information flows with respect to a variety of adversary models, the project is conducting user studies to determine if this approach reduces the gap between perceived and actual privacy.
The Project Team
- Dongwon Lee (PI): Penn State University
- Bo Luo (PI): University of Kansas
- Jun (Luke) Huan: University of Kansas
- Peng Liu: Penn State University
- Mary Beth Rosson: Penn State University
Publications and Deliverables
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