Nowadays,　outsourcing　data　and　machine　learning　tasks,　e.g.,　k-nearest　neighbor　(KNN)　classification,　to　clouds　has　become　a　scalable　and　cost-effective　way　for　large　scale　data　storage,　management,　and　processing.　However,　data　security　and　privacy　issue　have　been　a　serious　concern　in　outsourcing　data　to　clouds.　In　this　article,　we　propose　a　privacy-preserving　KNN　classification　scheme　on　cloud　data　in　a　twin-cloud　model　based　on　an　additively　homomorphic　cryptosystem　and　secret　sharing.　Compared　with　existing　works,　we　redesign　a　set　of　lightweight　building　blocks,　such　as　secure　square　Euclidean　distance,　secure　comparison,　secure　sorting,　secure　minimum,　and　maximum　number　finding,　and　secure　frequency　calculating,　which　achieve　the　same　security　level　but　with　higher　efficiency.　In　our　scheme,　data　owners　stay　offline,　which　is　different　from　secure-multiparty　computation-based　solutions　which　require　data　owners＇　stay　online　during　computation.　In　addition,　query　users　do　not　interact　with　the　cloud　except　sending　query　data　and　receiving　the　query　results.　Our　security　analysis　shows　that　the　scheme　protects　outsourced　data　security　and　query　privacy,　and　hides　access　patterns.　The　experiments　on　real-world　dataset　indicate　that　our　scheme　is　significantly　more　efficient　than　existing　schemes.