Millions of users place data about themselves on on-line social networks and, while probably they have an interest on some of this information to be publicly available, they certainly may consider some of this information shall remain confidential. Simultaneously, the data provides benefits as such data enables personalization which increases the quality of service; and thus, it is regularly analyzed with data mining techniques. Since privacy directly correlates to the control users have regarding the data about themselves, this paper provides a technique by which operators of on-line social networks can improve the service to their users by empowering the users to appraise the privacy risks that some information they provide results in others inferring confidential attributes.