When　enjoying　mobile　crowdsensing　(MCS),　it　is　vital　to　evaluate　the　trustworthiness　of　mobile　users　(MUs)　without　disclosing　their　sensitive　information.　However,　the　existing　schemes　ignore　this　requirement　in　the　multiple　crowdsourcers　(CSs)　scenario.　The　lack　of　a　credible　sharing　about　MUs＇　trustworthiness　results　in　an　inaccurate　trust　evaluation,　disabling　allocating　tasks　to　reliable　MUs.　To　address　it,　based　on　the　analysis　of　the　desired　properties,　we　propose　a　scheme　enabling　dependable　data　collection　with　multiple　crowdsourcers　trust　sharing　($D^{2}MTS$).　Specifically,　we　design　the　MU　anonymous　management.　Two　kinds　of　MU　generated　pseudonym　systems　without　relationships　are　presented　to　mark　each　MU　in　trust　evaluation　and　task　execution,　respectively.　Through　the　devised　pseudonym　changes　on　these　pseudonyms　and　the　common　token　distribution　algorithm,　$D^{2}MTS$　realizes　privacy-preserving　trust　sharing.　Moreover,　to　guarantee　credible　sharing,　based　on　the　hash　chain,　$D^{2}MTS$　records　MUs＇　trustworthiness　with　the　unforgeable　signature　on　the　blockchain　established　by　multiple　CSs　which　do　not　trust　each　other　naturally.　Extensive　experiments　show　that　compared　with　the　other　works,　$D^{2}MTS$＇s　detection　ratio　of　vicious　MUs　and　the　percentage　of　reliable　MUs　among　the　selected　ones　can　increase　by　208.61%　and　28.27%.　Both　computational　and　communication　delays　are　limited.　IEEE