Cloud-based　Convolutional　neural　network　(CNN)　is　a　powerful　tool　for　the　healthcare　center　to　provide　health　condition　monitor　service.　Although　the　new　service　has　future　prospects　in　the　medical,　patient＇s　privacy　concerns　arise　because　of　the　sensitivity　of　medical　data.　Prior　works　to　address　the　concern　have　the　following　unresolved　problems:　1)　focus　on　data　privacy　but　neglect　to　protect　the　privacy　of　the　machine　learning　model　itself;　2)　introduce　considerable　communication　costs　for　the　CNN　inference,　which　lowers　the　service　quality　of　the　cloud　server.　To　push　forward　this　area,　we　propose　PE-HEALTH,　a　privacy-preserving　health　monitor　framework　that　supports　fully-encrypted　CNN　(both　input　data　and　model).　In　PE-HEALTH,　the　medical　Internet　of　Things　(IoT)　sensor　serves　as　the　health　condition　data　collector.　For　protecting　patient　privacy,　the　IoT　sensor　additively　shares　the　collected　data　and　uploads　the　shared　data　to　the　cloud　server,　which　is　efficient　and　suited　to　the　energy-limited　IoT　sensor.　To　keep　model　privacy,　PE-HEALTH　allows　the　healthcare　center　to　previously　deploy,　and　then,　use　an　encrypted　CNN　on　the　cloud　server.　During　the　CNN　inference　process,　PE-HEALTH　does　not　need　the　cloud　servers　to　exchange　any　extra　messages　for　operating　the　convolutional　operation,　which　can　greatly　reduce　the　communication　cost.　©　2020　IEEE.