Graph　neural　networks　(GNNs)　have　been　widely　applied　in　various　graph　analysis　tasks.　To　provide　more　convenient　and　faster　predictive　services,　many　enterprises　are　choosing　to　deploy　GNNs　in　cloud　environments.　However,　given　the　increasing　privacy　concerns　about　GNNs　models　and　graph　data,　as　well　as　the　need　to　quickly　generate　embeddings　for　new　nodes　in　real-world　applications,　a　critical　issue　in　this　emerging　paradigm　is　to　ensure　the　security　and　scalability　of　GNN　predictions.　In　this　paper,　we　propose　a　privacy-preserving　and　scalable　GNN　prediction　scheme,　named　PS-GNN,　to　address　the　privacy　issues　in　cloud　environments.　Specifically,　PS-GNN　utilizes　a　customized　array　structure　to　store　graph　data　and　employs　secret　sharing　to　preserve　the　confidentiality　of　both　the　GNN　model　and　graph　data.　Besides,　the　scalability　of　PS-GNN　is　achieved　by　aggregating　feature　information　from　local　node　neighborhoods　in　parallel.　Through　a　detailed　analysis,　we　demonstrate　the　security　of　PS-GNN.　Extensive　experiments　on　real-world　datasets　demonstrate　that　PS-GNN　outperforms　existing　schemes　in　terms　of　computational　and　communication　overhead,　and　reaches　state-of-the-art　performance　on　large　graphs.