Differential　privacy　preserving　ensures　the　privacy　of　the　system　data　by　adding　certain　regular　noises　to　the　data　to　cover　up　the　real　information.　The　main　challenges　of　strong　nonlinearities,　uncertainty,　and　data　privacy　are　considered　together　for　switched　systems,　and　a　novel　privacy-preserving　robust　model-free　adaptive　predictive　control　(PPR-MFAPC)　method　is　proposed　that　guarantees　both　H-infinity　performance　and　system　privacy.　At　first,　a　performance-dependent　differential　privacy　noise　conforming　the　Laplace　distribution　is　designed,　which　can　adaptively　adjust　the　noise　size　to　balance　the　system　performance　and　privacy.　Then,　a　novel　privacy　level　analysis　with　evaluation　method　is　presented.　Subsequently,　the　strong　uncertainties　of　switched　systems　is　solved　through　a　dynamic　linearization　method.　On　this　basis,　a　novel　cost　function　is　designed　by　considering　both　the　H-infinity　performance　and　system　privacy　to　balance　the　system　performance　and　privacy　from　the　perspective　of　control　design.　Further,　by　incorporating　a　parameter　estimator　and　a　prediction　algorithm,　the　private　MFAPC　anti-noise　controller　is　obtained.　Finally,　the　feasibility　of　the　PPR-MFAPC　is　explained　with　illustrative　example.