An expert system to detect privacy's vulnerability of social networks
Gerges Tannous, Aziz M. Barbar · 2016
Over a decade ago, online social networks have emerged in people's lives. They have provided people with the tools to share information with their friends and families and took advantage of the data they have collected. Nowadays, more users are on the online social networks and many of them neglect their personal privacy and security. Many studies tried to influence those users and show them how vulnerable they were by approaching this issue from multiple sides such as connections, locations, images and text. In this solution, we try another perspective by introducing a fuzzy logic system whose role is to classify users into a vulnerability level based on the information they share and their recurrence. The fuzzy logic system implemented based on the Mamdani's technique is able to classify a given user's vulnerability and to show which areas of the shared information have led to the given conclusion. The provided solution still has many areas that can be improved such as building a crawler to collect information directly from the web accounts of the users and applying to the extracted data big data analytics techniques. Also, another area can be tackled and that is proving that users' vulnerability can be correlated.