Benefiting　from　the　computing　power　and　storage　power　of　cloud　computing,　machine　learning　tasks　usually　choose　to　upload　data　and　models　to　a　third-party　server.　However,　third-party　servers　may　face　data　privacy　leaks　in　the　process　of　data　acquisition,　storage,　and　use,　especially　in　the　fields　of　finance,　medical　treatment,　and　biometrics.　Homomorphic　encryption　technology　supports　ciphertext　calculation　without　decryption,　and　can　be　used　for　machine　learning　model　training　and　prediction　in　the　ciphertext　domain.　We　use　the　homomorphic　encryption　library　SEAL　to　reconstruct　the　linear　regression　protocol,　use　six　data　sets　to　conduct　experiments,　and　analyze　its　performance　and　security.　©　2022　The　Authors.　Published　by　Elsevier　B.V.