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.