Privacy is one of the fundamental issues when publishing or sharing social community data for social technology studies and business evaluation. Lately, researchers have developed privacy models much like okay-anonymity to save you node re-identification via shape data. However, even if those privacy fashions are enforced, an attacker may still have the ability to deduce one’s private statistics if a group of nodes in large part share the same touchy labels (i.e., attributes). In other words, the label-node courting isn't always nicely covered by pure structure anonymization methods. Moreover, current strategies, which depend upon area enhancing or node clustering, may also significantly alter key graph properties. Items shared through Social Media may affect more than one user’s privacy e.g., photos that depict multiple users comments that mention multiple users, events in which multiple users are invited, etc. The shortage of multi-celebration privateness management guide in modern mainstream Social Media infrastructures makes users unable to as it should be manipulated to whom those objects are absolutely shared or no longer. Computational mechanisms which can be capable of merge the privacy preferences of more than one customer’s right into an unmarried policy for an item can assist resolve this problem. But, merging more than one customers’ privateness preferences isn't always a smooth venture, because privateness options may war, so techniques to clear up conflicts are needed. To tackle this problem, in this, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media with privacy policy inference of user-uploaded images that is able to adapt to different situations by modeling the concessions that users make to reach a solution to the conflicts.