Privacy of Organization in Online Social Networks

With the rise in the participation of people on Online Social Networks (OSN), there has been a significant increase in reports of privacy leakage, both from users, as well as organizations. Although there are numerous solution frameworks for protecting user privacy, there are very few effective online tools to safeguard the privacy of organizations, most of which are based on manual altering of content. A lot of sensitive information of an organization can go public, intentionally or unintentionally. Analyzing and classifying the publicly available information will be very valuable to organizations. In this research, we were able to generate a privacy score for a social media post about a large organization. We utilized the data from internal Wiki, as well as public blogs of the organization, and evaluated the publicly available information on online social networks, especially, Twitter. We have used a Semantic Web representation for storing the data. The results show that a private information leak can be detected by our solution framework.