Abstract: With the increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users inadvertently shared personal information. Social Network is an emerging E-service for content sharing sites (CSS). It is emerging service which provides a reliable communication, through this communication a new attack ground for data hackers; they can easily misuses the data through these media. Some users over CSS affects users privacy on their personal contents, where some users keep on sending unwanted comments and messages by taking advantage of the users’ inherent trust in their relationship network. To discussing this we focus on the issue to protect user Images on social site. Images are travelling on social site so we decide policy how image will be travel on social network. We consider to decide how to decide policy where own image become protective. This proposes a privacy policy prediction and access restrictions along with blocking scheme for social sites using data mining techniques. Toward addressing this need, we propose a novel system to help users compose privacy settings for their images. To deal with this dilemma, our mechanism attempts to utilize users’ private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking their privacy.

Keywords: online social networks, FR system, open social, photo privacy.