Protection of User-Defined Sensitive Attributes on Online Social Networks Against Attribute Inference Attack via Adversarial Data Mining
File version
Accepted Manuscript (AM)
Author(s)
Reza, KJ
Islam, MZ
Estivill-Castro, V
Griffith University Author(s)
Year published
2020
Metadata
Show full item recordAbstract
Online social network (OSN) users share various types of personal information with other users. By analysing such personal information, a malicious data miner (or an attacker) can infer the sensitive information about the user which has not been disclosed publicly. This is generally known as attribute inference attack. In this study, we propose a privacy preserving technique, namely 3LP+, that can protect users’ multiple sensitive information from being inferred. We experimentally show that the 3LP+ algorithm can provide better privacy than an existing technique while maintaining the utility of users’ data.Online social network (OSN) users share various types of personal information with other users. By analysing such personal information, a malicious data miner (or an attacker) can infer the sensitive information about the user which has not been disclosed publicly. This is generally known as attribute inference attack. In this study, we propose a privacy preserving technique, namely 3LP+, that can protect users’ multiple sensitive information from being inferred. We experimentally show that the 3LP+ algorithm can provide better privacy than an existing technique while maintaining the utility of users’ data.
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Conference Title
Communications in Computer and Information Science
Volume
1221
Copyright Statement
© Springer Nature Switzerland AG 2020. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
Subject
Cybersecurity and privacy not elsewhere classified