To　conduct　necessary　aggregation　on　varying-quality　sensed　data　uploaded　by　workers　in　mobile　crowdsensing,　truth　discovery　technology　has　emerged　as　the　cornerstone　for　providing　precise　data　support　for　subsequent　applications.　Existing　studies　tend　to　adopt　local　differential　privacy　for　protection　against　potential　privacy　breaches,　but　often　ignore　the　influence　of　outliers　in　the　sensed　data　on　the　truth　discovery　accuracy　under　local　differential　privacy.　These　outliers　often　have　a　large　range　of　values,　resulting　in　a　large　amount　of　noise　in　the　injected　data.　Additionally,　due　to　workers’　concerns　about　privacy　breaches,　mobile　crowdsensing　servers　cannot　preprocess　data　without　privacy　protection.　To　this　end,　this　study　proposes　NATURE,　which　meets　local　differential　privacy　based　on　adaptive　pruning.　The　core　idea　of　the　algorithm　is　to　consider　the　noise　types　in　the　data　to　adaptively　prune　all　unnecessary　workers’　values　or　certain　task　values.　In　NATURE,　the　noise-aware　weight　and　importance　estimation　(NWIE)　method　based　on　a　formalized　constraint　optimization　problem　is　designed　to　facilitate　data　pruning.　Based　on　proving　the　optimal　pruning　problem　is　NP-hard,　this　study　designs　the　utility-aware　adaptive　pruning　(UAP)　method　with　polynomial　time　complexity　to　conduct　pruning.　Furthermore,　a　theoretical　analysis　of　NATURE’s　privacy,　utility,　and　complexity　is　carried　out.　Experimental　results　on　two　real-world　datasets　and　one　synthetic　dataset　demonstrate　that　NATURE　achieves　an　accuracy　improvement　of　at　least　20%　in　obtaining　＇truth＇　compared　to　its　comparative　algorithms.　©　2025　Chinese　Academy　of　Sciences.　All　rights　reserved.