Fine Grained Privacy Measuring of User's Profile Over Online Social Network

From last few years, social networking has become part of everyone's life. A person known to computer has its online social accounts like Facebook, Twitter, or MySpace. Considerable amount of work has been done to address privacy issues in online social networks. Many authors have discussed about information breach since users share their personal information which reveals their identity. A serious attention is required to pay to reduce privacy risk of users posed by their daily information sharing activities. There is no classified measure to measure privacy. This paper tries to quantify privacy and evaluate it. This paper also includes privacy issues raised from the individual user's viewpoint then propose a framework to compute the privacy index for a user on OSN [1] and shows the applicability and requirement of privacy index. This score indicates that the user is aware of their privacy profile or not. The index can be used for the recommendation to enhance the privacy settings of the users in the group. The framework proposes a mathematical model of basic commodity index to calculate privacy index of any user in OSN. Our definition of index takes an index number which is a number indicating sensitivity, more sensitive information a user discloses, the higher his or her privacy risk and so is its index number. The framework considers both sensitivity and visibility of information of user's profile and computes index value on the basis of them. The sensitivity of profile items over survey data is calculated using naive formula. Based on privacy measurement function values, the users on OSN are classified in three categories as Secure, Mediocre, and Vulnerable to privacy attack. We further compare the normal indexing technique to our privacy measurement function. This along with propose algorithm shows better efficacy and reduces the possibility false classification of user's categories.