In　spatial　crowdsourcing,　task　allocation　is　a　crucial　prerequisite　for　subsequent　location-aware　data　collection.　To　tackle　potential　location　privacy　breaches,　researchers　often　adopt　geo-indistinguishability.　Existing　approaches　that　satisfy　Geo-I　are　often　designed　for　one-to-one　scenarios,　while　implicitly　assume　that　workers　can　perform　any　task,　and　they　often　focus　on　minimizing　the　average　travel　distance,　rather　than　maximizing　the　number　of　task　allocation.　Furthermore,　these　studies　often　incorporate　the　planar　laplacian　mechanism　to　achieve　Geo-I.　However,　due　to　the　randomness　and　unbounded　nature　of　PL,　it　can　result　in　excessive　noise　in　the　location　data　uploaded　by　workers,　significantly　deteriorating　the　utility　of　task　allocation.　This　can　lead　to　either　long　distances　or　unassigned　tasks.　To　address　these　problems,　we　propose　MONITOR　(Many-to-many　task　allOcation　under　geo-iNdIsTinguishability　for　spatial　crOwdsouRcing),　a　new　privacy-preserving　task　allocation　approach　for　many-to-many　scenario.　The　general　idea　of　MONITOR　is　to　upload　the　distances　from　each　worker’s　true　location　to　the　obfuscated　preferred　tasks’　locations　instead　of　uploading　each　obfuscated　worker’s　location.　In　MONITOR,　to　collect　the　distances　for　subsequent　task　allocation,　we　design　an　obfuscated　distance　collection　method,　called　GroCol　(Group-based　obfuscated　distance　Collection).　To　improve　the　utility　for　task　allocation,　we　develop　a　parameter　independent　obfuscated　distance　comparison　method　called　ParCom　(Parameter-free　obfuscated　distance　Comparison).　To　illustrate　the　effectiveness　of　MONITOR,　we　first　theoretically　analyze　its　privacy　guarantee,　task　utility,　and　computational　complexity.　We　then　empirically　show　on　two　real-world　datasets　and　one　synthetic　dataset　that　MONITOR　share　similar　results　to　that　of　non-private　task　allocation　about　the　number　of　assigned　tasks,　and　reduce　the　average　travel　distance　by　more　than　20%　compared　to　the　baseline　approaches.　©　2025　Chinese　Institute　of　Electronics.　All　rights　reserved.