In　the　era　of　machine　learning,　mobile　users　are　able　to　submit　their　symptoms　to　doctors　at　any　time,　anywhere　for　personal　diagnosis.　It　is　prevalent　to　exploit　edge　computing　for　real-time　diagnosis　services　in　order　to　reduce　transmission　latency.　Although　data-driven　machine　learning　is　powerful,　it　inevitably　compromises　privacy　by　relying　on　vast　amounts　of　medical　data　to　build　a　diagnostic　model.　Therefore,　it　is　necessary　to　protect　data　privacy　without　accessing　local　data.　However,　the　blossom　has　also　been　accompanied　by　various　problems,　i.e.,　the　limitation　of　training　data,　vulnerabilities,　and　privacy　concern.　As　a　solution　to　these　above　challenges,　in　this　paper,　we　design　a　lightweight　privacy-preserving　medical　diagnosis　mechanism　on　edge.　Our　method　redesigns　the　extreme　gradient　boosting　(XGBoost)　model　based　on　the　edge-cloud　model,　which　adopts　encrypted　model　parameters　instead　of　local　data　to　reduce　amounts　of　ciphertext　computation　to　plaintext　computation,　thus　realizing　lightweight　privacy　preservation　on　resource-limited　edges.　Additionally,　the　proposed　scheme　is　able　to　provide　a　secure　diagnosis　on　edge　while　maintaining　privacy　to　ensure　an　accurate　and　timely　diagnosis.　The　proposed　system　with　secure　computation　could　securely　construct　the　XGBoost　model　with　lightweight　overhead,　and　efficiently　provide　a　medical　diagnosis　without　privacy　leakage.　Our　security　analysis　and　experimental　evaluation　indicate　the　security,　effectiveness,　and　efficiency　of　the　proposed　system.　©　2021　IEEE.