TY - JOUR
T1 - Community-structured evolutionary game for privacy protection in social networks
AU - Du, Jun
AU - Jiang, Chunxiao
AU - Chen, Kwang Cheng
AU - Ren, Yong
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received December 29, 2016; revised June 16, 2017 and September 15, 2017; accepted September 17, 2017. Date of publication October 2, 2017; date of current version December 19, 2017. This work was supported in part by the National Natural Science Foundation China under Project 61371079 and Project 61571300, in part by the U.S. Army Research Office under Grant W911NF-16-1-0448, and in part by the U.S. National Science Foundation under Grant CMMI-1435778. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Eduard A. Jorswieck. (Corresponding author: Chunxiao Jiang.) J. Du and Y. Ren are with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China (e-mail: blgdujun@gmail.com; reny@tsinghua.edu.cn).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/3
Y1 - 2018/3
N2 - Social networks have attracted billions of users and supported a wide range of interests and practices. Users of social networks can be connected with each other by different communities according to professions, living locations, and personal interests. With the development of diverse social network applications, academic researchers, and practicing engineers pay increasing attention to the related technology. As each user on the social network platforms typically stores and shares a large amount of personal data, the privacy of such user-related information raises serious concerns. Most research on privacy protection relies on specific information security techniques such as anonymization or access control. However, the protection of privacy depends heavily on the incentive mechanisms of social networks, like users' psychological decisions on security execution and socio-economic considerations. For example, the desire to influence the behaviors of other people may change a user's choice of security setting. In this paper, a game theoretic framework is established to model users' interactions that influence users' decisions as to whether to undertake privacy protection or not. To model the relationship of user communities, community-structured evolutionary dynamics are introduced, in which interactions of users can only happen among those users who have at least one community in common. Then the dynamics of the users' strategies to take a specific privacy protection or not is analyzed based on the proposed community structured evolutionary game theoretic framework. Experiments show that the proposed framework is effective in modeling the users' relationships and privacy protection behaviors. Moreover, results can also help social network managers to design appropriate security service and payment mechanisms to encourage their users to take the privacy protection, which can promote the spreading of privacy behavior throughout the network.
AB - Social networks have attracted billions of users and supported a wide range of interests and practices. Users of social networks can be connected with each other by different communities according to professions, living locations, and personal interests. With the development of diverse social network applications, academic researchers, and practicing engineers pay increasing attention to the related technology. As each user on the social network platforms typically stores and shares a large amount of personal data, the privacy of such user-related information raises serious concerns. Most research on privacy protection relies on specific information security techniques such as anonymization or access control. However, the protection of privacy depends heavily on the incentive mechanisms of social networks, like users' psychological decisions on security execution and socio-economic considerations. For example, the desire to influence the behaviors of other people may change a user's choice of security setting. In this paper, a game theoretic framework is established to model users' interactions that influence users' decisions as to whether to undertake privacy protection or not. To model the relationship of user communities, community-structured evolutionary dynamics are introduced, in which interactions of users can only happen among those users who have at least one community in common. Then the dynamics of the users' strategies to take a specific privacy protection or not is analyzed based on the proposed community structured evolutionary game theoretic framework. Experiments show that the proposed framework is effective in modeling the users' relationships and privacy protection behaviors. Moreover, results can also help social network managers to design appropriate security service and payment mechanisms to encourage their users to take the privacy protection, which can promote the spreading of privacy behavior throughout the network.
KW - Behavior spreading
KW - Community structure based evolutionary games
KW - Privacy protection
KW - Social networks
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U2 - 10.1109/TIFS.2017.2758756
DO - 10.1109/TIFS.2017.2758756
M3 - Article
AN - SCOPUS:85030755695
SN - 1556-6013
VL - 13
SP - 574
EP - 589
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
IS - 3
ER -