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＇　sen-sitive　data　directly.　Privacy-preserving　association　rule　mining　has　emerged　to　protect　users＇　privacy　dur-ing　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　multi-ple　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　evalu-ation　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.&　COPY;　2023　The　Author(s).　Published　by　Elsevier　B.V.　on　behalf　of　King　Saud　University.　This　is　an　open　access　article　under　the　CC　BY-NC-ND　license　(http://creativecommons.org/licenses/by-nc-nd/4.0/).