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- W3037933285 abstract "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." @default.
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- W3037933285 date "2020-01-01" @default.
- W3037933285 modified "2023-10-18" @default.
- W3037933285 title "Protection of User-Defined Sensitive Attributes on Online Social Networks Against Attribute Inference Attack via Adversarial Data Mining" @default.
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- W3037933285 doi "https://doi.org/10.1007/978-3-030-49443-8_11" @default.
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