Differential　privacy　is　an　effective　method　to　solve　data　privacy　leakage.　The　common　differential　privacy　method　is　achieved　by　adding　privacy　noises　to　the　transmitted　data,　which　may　affect　data　accuracy.　For　the　control　system,　data　accuracy　greatly　affects　the　system　performance.　To　circumvent　this　difficulty,　we　propose　a　novel　privacy-preserved　rolling　optimization　strategy　(PP-ROS)　for　switched　systems.　The　main　contributions　are　reflected　in　three　aspects:　1)　The　proposed　PP-ROS　is　used　to　calculate　the　private　control　input　by　adding　Laplace　noise　to　the　prediction　and　control　horizons,　instead　of　the　transmitted　data.　2)　Privacy　definitions　of　the　prediction　and　control　horizons　are　presented,　and　a　private　model　predictive　control　(P-MPC)　controller　design　is　provided　based　on　the　PP-ROS.　The　P-MPC　controller　achieves　the　privacy　of　its　parameters.　3)　Under　PP-ROS　and　P-MPC,　the　proof　and　calculation　methods　for　the　privacy　levels　of　control　input　and　system　output　are　given.　The　results　indicate　that　when　noise　is　added　to　the　horizons,　both　control　input　and　system　output　are　private.　Finally,　the　availability　and　benefits　of　PP-ROS　and　P-MPC　are　demonstrated　using　two　simulation　examples　and　comparison　results.