Symmetric　Searchable　Encryption　(SSE),　as　an　ideal　primitive,　can　ensure　data　privacy　while　supporting　retrieval　over　encrypted　data.　However,　existing　multi-user　SSE　schemes　require　the　data　owner　to　share　the　secret　key　with　all　query　users　or　always　be　online　to　generate　search　tokens.　While　there　are　some　solutions　to　this　problem,　they　have　at　least　one　weakness,　such　as　non-supporting　conjunctive　query,　result　decryption　assistance　of　the　data　owner,　and　unauthorized　access.　To　solve　the　above　issues,　we　propose　an　Owner-free　Distributed　Symmetric　searchable　encryption　supporting　Conjunctive　query　(ODiSC).　Specifically,　we　first　evaluate　the　Learning-Parity-with-Noise　weak　Pseudorandom　Function　(LPN-wPRF)　in　dual-cloud　architecture　to　generate　search　tokens　with　the　data　owner　free　from　sharing　key　and　being　online.　Then,　we　provide　fine-grained　conjunctive　query　in　the　distributed　architecture　using　additive　secret　sharing　and　symmetric-key　hidden　vector　encryption.　Finally,　formal　security　analysis　and　empirical　performance　evaluation　demonstrate　that　ODiSC　is　adaptively　simulation-secure　and　efficient.