Online Social Networks have become a significant source to store personal information. A recent addition to this space, geo-social networks (GSNs) such as Yelp or Foursquare, collects user locations, through check-ins performed by users at visited venues. Overtly, personal information allows GSN providers to offer a variety of applications, including personalized recommendations and targeted advertising, and venue owners to promote their businessthrough spatio-temporal incentives. Without privacy people may be reluctant to use geo-social networks; without user information the provider unable to use applications and have no incentive to participate in geosocial networks. In this paper, here PROFILER, a framework for constructing location centric profiles (LCP), aggregates built over the profiles of users that have visited discrete locations (i.e., venues) and a set of co-located users is introduced. PROFIR endows users with strong privacy guarantees and providers with correctness assurances. In addition to a venue centric approach, in thisa proposeddecentralized solution for computing real time LCP snapshots over the profiles is implemented.