Recruiting　users　in　mobile　crowdsensing　(MCS)　can　make　the　platform　obtain　high-quality　data　to　provide　better　services.　Although　the　privacy　leakage　during　the　process　of　user　recruitment　has　received　a　lot　of　research　attention,　none　of　the　existing　work　considers　the　evaluation　of　the　sensing　quality　of　privacy-preserving　data　submitted　by　users,　which　makes　the　platform　incapable　of　recruiting　users　suitably　to　obtain　high-quality　sensing　data,　thereby　reducing　the　reliability　of　MCS　services.　To　solve　this　problem,　we　first　propose　a　sensing　quality　evaluation　method　based　on　the　deviation　and　variance　of　sensing　data.　According　to　it,　the　platform　can　obtain　the　sensing　quality　of　privacy-preserving　data　for　each　user　during　the　recruitment.　Then　we　model　the　user　recruitment　with　a　limited　budget　platform　as　a　Combinatorial　Multi-Armed　Bandit　(CMAB)　game　to　determine　the　recruited　users　based　on　the　sensing　quality　of　data　obtained　by　evaluation.　Finally,　we　theoretically　prove　that　our　algorithm　satisfies　differential　privacy　and　the　upper　bound　on　the　regret　of　rewards　is　restricted.　Experimental　results　show　that　our　proposal　is　superior　in　various　properties,　and　our　method　has　a　73.67%　advantage　in　accumulated　sensing　qualities　compared　with　comparison　schemes.