Preserving Privacy in Social Networks Against Connection Fingerprint Attacks

Existing works on identity privacy protection on social networks make the assumption that all the user identities in a social network are private and ignore the fact that in many real-world social networks, there exists a considerable amount of users such as celebrities, media users, and organizatio...

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Main Authors: WANG, Yazhe, ZHENG, Baihua
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/2482
http://dx.doi.org/10.1109/ICDE.2015.7113272
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spelling sg-smu-ink.sis_research-34812015-12-04T15:45:49Z Preserving Privacy in Social Networks Against Connection Fingerprint Attacks WANG, Yazhe ZHENG, Baihua Existing works on identity privacy protection on social networks make the assumption that all the user identities in a social network are private and ignore the fact that in many real-world social networks, there exists a considerable amount of users such as celebrities, media users, and organization users whose identities are public. In this paper, we demonstrate that the presence of public users can cause serious damage to the identity privacy of other ordinary users. Motivated attackers can utilize the connection information of a user to some known public users to perform re-identification attacks, namely connection fingerprint (CFP) attacks. We propose two k-anonymization algorithms to protect a social network against the CFP attacks. One algorithm is based on adding dummy vertices. It can resist powerful attackers with the connection information of a user with the public users within n hops (n ≥ 1) and protect the centrality utility of public users. The other algorithm is based on edge modification. It is only able to resist attackers with the connection information of a user with the public users within 1 hop but preserves a rich spectrum of network utility. We perform comprehensive experiments on real-world networks and demonstrate that our algorithms are very efficient in terms of the running time and are able to generate k-anonymized networks with good utility. 2015-04-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/2482 info:doi/10.1109/ICDE.2015.7113272 http://dx.doi.org/10.1109/ICDE.2015.7113272 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Edge modification K-anonymization Network utility Privacy protection Re identifications Real-world networks Running time User identity Computer Sciences Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Edge modification
K-anonymization
Network utility
Privacy protection
Re identifications
Real-world networks
Running time
User identity
Computer Sciences
Databases and Information Systems
spellingShingle Edge modification
K-anonymization
Network utility
Privacy protection
Re identifications
Real-world networks
Running time
User identity
Computer Sciences
Databases and Information Systems
WANG, Yazhe
ZHENG, Baihua
Preserving Privacy in Social Networks Against Connection Fingerprint Attacks
description Existing works on identity privacy protection on social networks make the assumption that all the user identities in a social network are private and ignore the fact that in many real-world social networks, there exists a considerable amount of users such as celebrities, media users, and organization users whose identities are public. In this paper, we demonstrate that the presence of public users can cause serious damage to the identity privacy of other ordinary users. Motivated attackers can utilize the connection information of a user to some known public users to perform re-identification attacks, namely connection fingerprint (CFP) attacks. We propose two k-anonymization algorithms to protect a social network against the CFP attacks. One algorithm is based on adding dummy vertices. It can resist powerful attackers with the connection information of a user with the public users within n hops (n ≥ 1) and protect the centrality utility of public users. The other algorithm is based on edge modification. It is only able to resist attackers with the connection information of a user with the public users within 1 hop but preserves a rich spectrum of network utility. We perform comprehensive experiments on real-world networks and demonstrate that our algorithms are very efficient in terms of the running time and are able to generate k-anonymized networks with good utility.
format text
author WANG, Yazhe
ZHENG, Baihua
author_facet WANG, Yazhe
ZHENG, Baihua
author_sort WANG, Yazhe
title Preserving Privacy in Social Networks Against Connection Fingerprint Attacks
title_short Preserving Privacy in Social Networks Against Connection Fingerprint Attacks
title_full Preserving Privacy in Social Networks Against Connection Fingerprint Attacks
title_fullStr Preserving Privacy in Social Networks Against Connection Fingerprint Attacks
title_full_unstemmed Preserving Privacy in Social Networks Against Connection Fingerprint Attacks
title_sort preserving privacy in social networks against connection fingerprint attacks
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/2482
http://dx.doi.org/10.1109/ICDE.2015.7113272
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