Privacy-preserving　truth　discovery,　as　a　data　aggregation　algorithm　that　can　extract　reliable　results　from　disparate　and　conflicting　data　in　a　privacy-preserving　manner,　has　received　a　lot　of　attention　in　ensuring　the　reliability　and　privacy　of　data　in　mobile　crowdsensing　systems.　However,　most　of　the　existing　work　requires　that　workers　must　stay　online　all　the　time　during　the　full　process　of　truth　discovery.　Although　a　few　recent　schemes　have　been　proposed　to　tolerate　worker　dropout,　they　are　tailored　for　a　single-round　setting.　Repeating　these　schemes　several　times　to　adapt　to　the　truth　discovery　will　introduce　significant　computational　and　communication　overheads,　especially　for　the　workers.　To　solve　the　above　challenges,　in　this　article,　we　propose　a　multiround　efficient　and　secure　truth　discovery　scheme　in　mobile　crowdsensing　systems　that　can　balance　the　3-way　tradeoff　between　privacy　protection,　dropout　tolerance,　and　protocol　efficiency.　Specifically,　we　devise　a　novel　mask　generation　capable　of　reusing　secrets　to　eliminate　the　costly　overhead　of　workers　needing　to　recompute　new　secrets　each　round.　Besides,　we　design　a　lightweight　dropout　tolerance　mechanism　to　guarantee　that　even　if　workers　drop　out　halfway,　the　server　can　still　acquire　meaningful　truth.　Rigorous　security　analysis　and　extensive　experimental　results　demonstrate　the　privacy　and　efficiency　of　our　scheme,　respectively.