Mobile　crowdsensing　(MCS)　enables　a　platform　to　recruit　users　to　collectively　perform　sensing　tasks　from　requesters.　In　order　to　maximize　the　completion　qualities　of　tasks,　an　incentive　mechanism　should　be　well　designed　for　the　platform　to　incentivize　high-quality　users＇　participation.　The　existing　works　largely　adopt　the　Stackelberg　game　to　model　the　strategic　interactions　in　the　incentive　mechanism.　However,　there　are　practical　issues　that　are　less　investigated　in　the　context　of　the　Stackelberg-based　incentive　mechanism.　First,　the　platform　has　no　knowledge　about　users＇　sensing　qualities　beforehand　due　to　their　private　information.　Second,　the　platform　needs　users＇　continuous　participation　in　the　long　run,　which　results　in　fairness　requirements.　Third,　it　is　also　crucial　to　protect　users＇　privacy　due　to　the　potential　privacy　leakage　concerns　(e.g.,　sensing　qualities)　after　completing　tasks.　In　this　article,　we　jointly　address　these　issues　and　propose　the　three-stage　Stackelberg-based　incentive　mechanism　for　the　platform　to　recruit　participants.　In　detail,　we　leverage　combinatorial　volatile　multiarmed　bandits　(CVMABs)　to　elicit　unknown　users＇　sensing　qualities.　We　use　the　drift-plus-penalty　(DPP)　technique　in　Lyapunov　optimization　to　handle　the　fairness　requirements.　We　blur　the　quality　feedback　with　tunable　Laplacian　noise　such　that　the　incentive　mechanism　protects　locally　differential　privacy　(LDP).　Finally,　we　carry　out　experiments　to　evaluate　our　incentive　mechanism.　The　numerical　results　show　that　our　incentive　mechanism　achieves　sublinear　regret　performance　to　learn　unknown　quality　with　fairness　and　privacy　guarantee.