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Privacy has been a big concern for users of social network services (SNS). On recent criticism about privacy protection, most SNS now provide fine privacy controls, allowing users to set visibility levels for almost every profile item. However, this also creates a number of difficulties for users. First, SNS providers often set most items by default to the highest visibility to improve the utility of social network, which may conflict with users' intention. It is often formidable for a user to fine-tune tens of privacy settings towards the user desired settings. Second, tuning privacy settings involves an intricate tradeoff between privacy and utility. When you turn off the visibility of one item to protect your privacy, the social utility of that item is turned off as well. It is challenging for users to make a tradeoff between privacy and utility for each privacy setting. We propose a framework for users to conveniently tune the privacy settings towards the user desired privacy level and social utilities. It mines the privacy settings of a large number of users in a SNS, e.g., Facebook, to generate latent trait models for the level of privacy concern and the level of utility preference. A tradeoff algorithm is developed for helping users find the optimal privacy settings for a specified level of privacy concern and a personalized utility preference. We crawl a large number of Facebook accounts and derive the privacy settings with a novel method. These privacy setting data are used to validate and showcase the proposed approach.