Traffic　estimation　is　a　popular　approach　to　acquire　traffic　conditions　in　urban　areas.　At　present,　using　the　traffic　data　to　realize　the　low-cost　traffic　estimation　has　already　been　widely　favored.　Although　those　data　include　various　sensitive　element,　people　ignore　the　harm　caused　by　information　leakage　while　the　data　are　used.　Additionally,　the　transmission　of　vehicle　data　also　requires　a　very　large　communication　bandwidth.　To　address　those　problems,　we　focus　on　the　privacy-preserving　vehicle　data　and　reducing　the　amount　of　ciphertext　data　to　achieve　a　city-scale　traffic　estimation.　Meanwhile,　we　present　a　novel　framework　that　integrates　compressive　sensing　(CS)　technology　into　privacy-　preserving　vehicle　data.　Furthermore,　outsourcing　vehicle　data　to　the　cloud　is　adopted　to　overcome　the　limitations　of　the　in-vehicle　sensors.　In　particular,　we　present　a　feasible　computational　scheme　for　traffic　estimation,　further　improve　the　capacity　of　privacy-　preserving　and　decrease　system　energy　consumption.　Finally,　we　validate　the　effectiveness　of　the　scheme　proposed　through　the　real-world　dataset.　©　2019　IEEE.