ABSTRACT:
Releasing social network data could
seriously breach user privacy. User profile and friendship relations are
inherently private. Unfortunately, it is possible to predict sensitive
information carried in released data latently by utilizing data mining
techniques. Therefore, sanitizing network data prior to release is necessary.
In this paper, we explore how to launch an inference attack exploiting social
networks with a mixture of non-sensitive attributes and social relationships.
We map this issue to a collective classification problem and propose a
collective inference model. In our model, an attacker utilizes user profile and
social relationships in a collective manner to predict sensitive information of
related victims in a released social network dataset. To protect against such
attacks, we propose a data sanitization method collectively manipulating user
profile and friendship relations. The key novel idea lies that besides
sanitizing friendship relations, the proposed method can take advantages of
various data-manipulating methods. We show that we can easily reduce
adversary’s prediction accuracy on sensitive information, while resulting in
less accuracy decrease on non-sensitive information towards three social
network datasets. To the best of our knowledge, this is the first work that
employs collective methods involving various data-manipulating methods and
social relationships to protect against inference attacks in social networks.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Zhipeng Cai, Senior Member, IEEE, Zaobo
He, Xin Guan, and Yingshu
Li, Senior Member, IEEE, “Collective Data-Sanitization for Preventing Sensitive
Information Inference Attacks in Social Networks”, IEEE Transactions on
Dependable and Secure Computing