What about Privacy of My OSN Data?

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ABSTRACTIn the arena of internet of things, everyone has the ability to share every aspect of their lives with other people. Social media is the most popular and effective medium to provide communication. Social media has gripped our lives in a dramatic way. Privacy of users data lying with the service providers needs to be preserved when published for the purpose of research as the release of sensitive personal information of an individual may pose security threats. This has become an important research area nowadays. To some extent, the concepts of anonymization that were earlier used to preserve privacy of relational microdata have been applied to preserve privacy of social networks data. Anonymizing social networks data is challenging as it is a complex structure with users connected to one another graphically and the most important is to preserve the structural properties of the graph depicting the social network relationships while applying such concepts. Recent studies based upon K-anonymity and L-diversity help to preserve privacy of online social networks data and subsequently identify attacks that arise while applying these techniques in different scenarios. K-anonymity equalizes the degree of the nodes to prevent the data from identity disclosure but it cannot preserve sensitive information and also cannot handle attacks arising due to background knowledge and homogeneity. To cope up with the drawbacks of K anonymity, L-diversity was introduced that protects the sensitive labels of the users. In this paper, a novel technique has been proposed which implements the combined features of K-anonymity and L-diversity. Our proposed approach has been validated using the data of real time social network–Twitter (most popular microblogging network). The performance of the proposed technique has been measured by the metrics, such as average path length, average change in sensitive labels, and remaining ratio of top influential users. It thus becomes evident from the results that the values of these parameters attained with the proposed technique for the anonymized graph has minimal variation to that of original structural graph. So, it is possible to retain the utility without compromising privacy while publishing social networks data. Further, the performance of the proposed technique has been discussed by calculating the information loss that addresses the concern of preserving privacy with the least variation of actual content viz info loss.