Truth　discovery　has　received　considerable　attention　in　mobile　crowdsensing　systems.　In　real　practice,　it　is　vital　to　resolve　conflicts　among　a　large　amount　of　sensory　data　and　estimate　the　truthful　information.　Although　truth　discovery　has　been　widely　explored　to　improve　aggregation　accuracy,　numerous　security　and　privacy　issues　still　need　to　be　addressed.　Existing　schemes　either　do　not　guarantee　the　privacy　of　each　participating　user,　or　fail　to　consider　practical　needs　in　crowdsensing　systems.　In　this　paper,　we　present　two　reliable　and　privacy-preserving　truth　discovery　schemes　for　different　scenarios.　Our　first　design　is　fit　for　applications　where　users　are　relatively　stable.　By　employing　the　homomorphic　Paillier　encryption,　one-way　hash　chain,　and　super-increasing　sequence　techniques,　this　approach　not　only　guarantees　strong　privacy,　but　also　is　highly　efficient　and　practical.　Our　second　design　suits　applications　where　users　are　frequently　moving.　In　such　an　application,　we　explore　data　perturbation　and　homomorphic　Paillier　encryption　to　shift　all　user　workloads　to　the　server　side,　without　compromising　users＇　privacy.　Through　detailed　security　analysis,　we　demonstrate　that　both　schemes　are　secure,　practical,　and　privacy-preserving.　Moreover,　extensive　experiments　based　on　real　world　and　simulated　mobile　crowdsensing　systems,　we　demonstrate　the　efficiency　of　our　proposed　schemes.