This chapter discusses critical privacy challenges in social intelligence systems when the collective social intelligence data meets privacy constraints. We examine how the collection and integration of sensitive personal information across platforms raise significant privacy concerns which potentially limit the effectiveness of social intelligence solutions. To address these challenges, we present two novel solutions: CoviDKG and FaceCrowd. CoviDKG is a distributed knowledge graph framework that constructs a set of knowledge graphs from individual sources/platforms and exchanges the privacy-aware information across different sources/platforms to effectively detect online false information. FaceCrowd is a web crowdsourcing-based face partition approach that aims to improve the performance of current face recognition models in social intelligence by designing a novel crowdsourced partial face graph generated from privacy-preserved social media face images. Through extensive experiments on real-world datasets and user studies, we demonstrate that both frameworks successfully balance privacy protection with superior performance.