Assigning　sensing　tasks　to　appropriate　task　participants　is　critical　for　mobile　crowdsensing　(MCS)　and　is　an　essential　optimization　problem.　However,　existing　task　assignment　(or　say　participant　selection)　solutions　for　MCS　generally　support　a　single-objective　optimization　(e.g.,　minimizing　travel　distance　or　maximizing　social　welfare).　Additionally,　task　assignment　for　MCS　usually　requires　task　participants＇　and　a　task　requester＇s　location　information,　which　compromises　their　location　privacy　and　hinders　participation　willingness.　To　achieve　task　assignment　with　bi-objective　optimization　and　safeguard　bilateral　privacy,　in　this　paper,　we　propose　RATE,　a　privacy-preserving　task　assignment　with　bi-objective　optimization　for　MCS.　RATE　features　the　following　characteristics.　First,　RATE　enables　task　assignment　with　bi-objective　optimization　including　maximizing　the　social　welfare　and　the　requester＇s　revenue,　simultaneously.　Second,　RATE　achieves　bilateral　privacy-preserving　task　assignment　with　bi-objective　optimization　by　carefully　designing　underlyingly　secure　computing　protocols.　Third,　RATE　approximates　optimal　results　of　task　assignments　without　sacrificing　privacy.　Theoretical　analyses　show　that　RATE　protects　the　location　privacy　of　both　the　task　requester　and　the　task　participants.　Meanwhile,　experimental　evaluations　demonstrate　that　RATE　outperforms　traditional　task　assignment　solutions　and　generates　the　task　assignment　result　effectively　and　efficiently.