Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.
privacy breaches many anonymization techniques [4] are adapted while publishing the Social Network data. Even after anonymization, several attacks are possible to obtain vital information and identification of particular users, which is a threat to the purpose of anonymization and security, so the information must be protected. Walk based attacks[2] are one of the most prominent attacks on a social networking graph. We have proposed an algorithm that prevents walk based attacks to the large extent yet minimizing the data loss and thus retaining the data mining quality of the social graph to a considerable extent. We have used "user characteristics metric" along with Ant Colony Optimization technique to anonymize the social network data, maintaining the aforesaid criteria.The rest of the paper is organized as follows: in section II we discuss various types of attacks and their mathematical notations. In section III and IV we discuss our algorithm and expected results and analysis respectively. And finally we conclude in section V.Abstract: Social network is one of the most impactful innovations of the last decade. It gives a way to connect millions of people around the world. Social networking sites sometimes sell their data to third party organizations for analysis and data mining, as a result of which, there is a chance that privacy of the users are compromised. Even after naive anonymization of the social graph, several attacks are possible to identify the victim and hence his private information can be extracted. Walk based attacks are one of the most prominent active attacks on a social networking graph. Where the attacker creates a set of malicious nodes before naive anonymization and attaches them to a target node creating an identifiable subgraph. Then in the naive anonymized graph it tries to identify the subgraph, if it can do so then the identity of the victim is compromised. We have proposed an algorithm that prevents walk based attacks to a large extent yet minimizing the data loss thus retaining the data mining quality of the social graph to a considerable extent. We have used "user characteristics metric" along with Ant Colony Optimization to anonymize our data maintaining the aforesaid criteria.
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