Data　security　has　always　been　one　of　the　public　concerns,　and　homomorphic　encryption　technology,　as　one　of　the　effective　means　to　ensure　data　security,　has　always　been　a　hot　topic　of　academic　research.　Traditional　machine　learning　is　trained　in　a　batch　environment.　In　practice,　data　owners　tend　to　outsource　data　and　model　training　tasks　to　third-party　clouds　with　huge　computing　resources,　but　data　outsourcing　carries　the　risk　of　privacy　leakage.　Therefore,　in　order　to　prevent　this　situation,　an　effective　method　is　to　encrypt　the　data　before　the　outsourcing,　and　the　third-party　cloud　trains　the　machine　model　of　the　ciphertext　data.　This　method　has　special　requirements　for　encryption　algorithms　which　should　support　computation　directly　on　the　ciphertext　data.　This　paper　presents　a　novel　data　flow　computing　privacy　protection　framework　based　on　the　homomorphic　encryption　algorithm　CKKS　and　the　flow　data　processing　engine　Flink,　called　SDPPF　(Streaming　Data　Privacy　Protection　on　Flink).　The　proposed　framework　supports　the　functions　of　data　stream　encryption,　decryption　and　ciphertext　computing,　and　expands　the　functions　of　vector　point　multiplication　and　array　sum　operation　on　the　basic　simultaneous　operation　supported　by　the　original　CKKS　algorithm.　This　paper　also　selects　a　classical　machine　learning　algorithm:　KNN　algorithm,　combined　with　the　homomorphic　encryption　algorithm　CKKS　to　realize　the　privacy　protection　machine　learning　algorithm.　©　2023　SPIE.