To　obtain　reliable　results　from　conflicting　data　in　mobile　crowdsensing,　numerous　truth　discovery　protocols　have　been　proposed　in　the　past　decade.　However,　most　of　them　do　not　consider　the　data　privacy　of　entities　involved　(e.g.,　workers　and　servers),　and　several　existing　privacy-preserving　truth　discovery　protocols　either　provide　limited　privacy　protection　or　have　heavy　computation　and　communication　overheads　due　to　iterative　computation　and　transmission　over　large　ciphertexts.In　this　paper,　we　aim　to　propose　privacy-preserving　and　lightweight　truth　discovery　protocols　to　tackle　the　above　problems.　Specifically,　we　carefully　design　an　anonymization　protocol　named　AnonymTD　to　delink　workers　from　their　data,　where　workers＇　data　are　computed　and　transmitted　without　complicated　encryption.　To　further　reduce　each　worker＇s　overheads　in　the　scenarios　where　workers　are　willing　to　share　their　weights,　we　resort　to　the　perturbation　technology　to　propose　a　more　lightweight　truth　discovery　protocol　named　PerturbTD.　Based　on　workers＇　perturbed　data,　two　cloud　servers　in　PerturbTD　complete　most　of　the　workload　of　truth　discovery　together,　which　avoids　the　frequent　involvement　of　workers.　The　theoretical　analysis　and　the　comparative　experiments　in　this　paper　demonstrate　that　our　two　protocols　can　achieve　our　security　goals　with　low　computation　and　communication　overheads.　　©　2023　IEEE.