This document proposes a framework called PrivRank for privacy-preserving publication of social media data to enable personalized recommendations while protecting private user data. PrivRank continuously obfuscates user activity data to minimize leakage of private attributes like gender, within a set level of acceptable data distortion to preserve recommendation utility. An evaluation shows PrivRank provides stronger privacy protections than existing methods while better maintaining data quality for recommendations.