Privacy Protection in Social Networks: Bridging the Gap Between User Perception and Privacy Enforcement

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.

The Project Team

Publications and Deliverables

  • #DontTweetThis: Scoring Private Information in Social Networks By Qiaozhi Wang, Hao Xue, Fengjun Li, Dongwon Lee, and Bo LuoAbstract: With the growing popularity of online social networks, a large amount of private or sensitive information has ...
    Posted May 29, 2019, 12:01 PM by Bo Luo
  • Private Browsing Mode Not Really That Private: Dealing with Privacy Breach Caused by Browser Extensions By B. Zhao, and P. LiuIn 45th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), June 2015, Brazil
    Posted Apr 8, 2015, 6:57 PM by Bo Luo
  • Exploring Tag-based Like Networks By Kyungsik Han, Jin Yea Jang, Dongwon LeeIn ACM Conf. on Human Factors in Computing Systems (CHI), Seoul, Korea, April 2015 (Work-in-Progress)Abstract: The emergence of social ...
    Posted Feb 2, 2015, 6:20 PM by Bo Luo
  • Generation Like: Comparative Characteristics in Instagram Jin Yea Jang, Kyungsik Han, Patrick C. Shih, Dongwon LeeIn ACM Conf. on Human Factors in Computing Systems (CHI), Seoul, Korea, April 2015. Acceptance Rate: 23%Abstract: The emergence ...
    Posted Feb 2, 2015, 6:19 PM by Bo Luo
  • Automatic Social Circle Detection Using Multi-View Clustering Yuhao Yamg, Chao Lan, Xiali Li, Bo Luo, and Jun HuanIn ACM Conf. on Information and Knowledge Management (CIKM), 2014 (accepted).Abstract:With the development of information technology, online ...
    Posted Sep 2, 2014, 3:02 PM by Bo Luo
Showing posts 1 - 5 of 6. View more »

Project News

  • NSF Grand Awarded The project is now supported by NSF SaTC program: NSF CNS-1422215 and NSF CNS-1422206.
    Posted Sep 2, 2014, 3:22 PM by Bo Luo
Showing posts 1 - 1 of 1. View more »

Bo Luo,
May 10, 2019, 3:55 PM