Association　rule　mining　is　an　efficient　method　to　mine　the　association　relationships　between　different　items　from　large　transaction　databases,　but　is　vulnerable　to　privacy　leakage　as　operates　over　users’　sensitive　data　directly.　Privacy-preserving　association　rule　mining　has　emerged　to　protect　users’　privacy　during　rule　mining.　Unfortunately,　existing　privacy-preserving　association　rule　mining　schemes　suffer　from　high　overhead,　fail　to　support　multiple　users,　and　are　challenging　to　prevent　collusion　attacks　between　twin-server.　To　this　end,　in　this　paper,　we　propose　a　privacy-preserving　association　rule　mining　solution　via　multi-key　fully　homomorphic　encryption　over　the　torus　(MKTFHE),　which　efficiently　supports　multiple　users　through　a　single　server　only.　Specifically,　we　first　construct　some　multi-key　homomorphic　gates　based　on　MKTFHE.　Then,　we　designed　a　series　of　privacy-preserving　computational　protocols　based　on　multi-key　homomorphic　gates.　Finally,　we　build　a　privacy-preserving　association　rule　mining　system　with　a　single　cloud　server　to　support　multiple　users.　Moreover,　privacy　analysis　and　performance　evaluation　demonstrate　our　proposal　is　efficient　and　feasible.　In　contrast　to　existing　solutions,　the　proposed　scheme　outperforms　encryption　and　communication,　saving　approximately　8.5%　running　time.　©　2023　The　Author(s)