Providing　appropriate　monetary　rewards　is　an　efficient　way　for　mobile　crowdsensing　to　motivate　the　participation　of　task　participants.　However,　a　monetary　incentive　mechanism　is　generally　challenging　to　prevent　malicious　task　participants　and　a　dishonest　task　requester.　Moreover,　prior　quality-aware　incentive　schemes　are　usually　failed　to　preserve　the　privacy　of　task　participants.　Meanwhile,　most　existing　privacy-preserving　incentive　schemes　ignore　the　data　quality　of　task　participants.　To　tackle　these　issues,　we　propose　a　privacy-preserving　and　data　quality-aware　incentive　scheme,　called　PACE.　In　particular,　data　quality　consists　of　the　reliability　and　deviation　of　data.　Specifically,　we　first　propose　a　zero-knowledge　model　of　data　reliability　estimation　that　can　protect　data　privacy　while　assessing　data　reliability.　Then,　we　quantify　the　data　quality　based　on　the　deviation　between　reliable　data　and　the　ground　truth.　Finally,　we　distribute　monetary　rewards　to　task　participants　according　to　their　data　quality.　To　demonstrate　the　effectiveness　and　efficiency　of　PACE,　we　evaluate　it　in　a　real-world　dataset.　The　evaluation　and　analysis　results　show　that　PACE　can　prevent　malicious　behaviors　of　task　participants　and　a　task　requester,　and　achieves　both　privacy-preserving　and　data　quality　measurement　of　task　participants.