Privacy-preserving　online　multi-task　assignment　is　a　crucial　aspect　of　spatial　crowdsensing　on　untrusted　platforms,　where　multiple　real-time　tasks　are　allocated　to　appropriate　workers　in　a　privacy-preserving　manner.　While　existing　schemes　ensure　the　privacy　of　tasks　and　users,　they　seldom　focus　on　minimizing　the　total　moving　distances　for　crowdsensing　workers　when　assigning　multiple　tasks　in　real　time,　which　adversely　impacts　the　efficiency　of　online　multi-task　assignments.　To　address　this　issue,　we　propose　POTA,　the　first　privacy-preserving　online　multi-task　assignment　scheme　with　path　planning　that　minimizes　the　total　moving　distances　for　crowdsensing　workers　without　additional　noise.　POTA　cryptographically　implements　the　extended　minimum-cost　flow　model,　which　models　the　encrypted　data　of　workers　and　tasks　in　a　graph　and　later　produces　optimized　routing.　With　such　a　secure　path-planning　component,　POTA　reduces　the　total　moving　distances　by　25.19\%-52.78\%25.19%-52.78%　in　the　tested　dataset　compared　with　the　state-of-the-art　schemes　with　obfuscated　path　planning.　Security　analysis　proves　that　POTA　guarantees　the　confidentiality　of　sensitive　data,　a　stronger　security　property　than　introducing　obfuscation　to　sensitive　data.　Experimental　evaluations　on　real-world　datasets　demonstrate　the　feasibility　of　POTA　in　terms　of　running　time　and　its　ability　to　achieve　minimized　total　moving　distances.