With　the　development　of　machine　learning,　it　is　popular　that　mobile　users　can　submit　individual　symptoms　at　any　time　anywhere　for　medical　diagnosis.　Edge　computing　is　frequently　adopted　to　reduce　transmission　latency　for　real-time　diagnosis　service.　However,　the　data-driven　machine　learning,　which　requires　to　build　a　diagnosis　model　over　vast　amounts　of　medical　data,　inevitably　leaks　the　privacy　of　medical　data.　It　is　necessary　to　provide　privacy　preservation.　To　solve　above　challenging　issues,　in　this　article,　we　design　a　lightweight　privacy-preserving　medical　diagnosis　mechanism　on　edge,　called　LPME.　Our　LPME　redesigns　the　extreme　gradient　boosting　(XGBoost)　model　based　on　the　edge-cloud　model,　which　adopts　encrypted　model　parameters　instead　of　local　data　to　remove　amounts　of　ciphertext　computation　to　plaintext　computation,　thus　realizing　lightweight　privacy　preservation　on　resource-limited　edge.　In　addition,　LPME　provides　secure　diagnosis　on　edge　with　privacy　preservation　for　private　and　timely　diagnosis.　Our　security　analysis　and　experimental　evaluation　indicates　the　security,　effectiveness,　and　efficiency　of　LPME.