To　motivate　data　owner　(DO)　to　trade　data,　the　existing　data　trading　allows　DO　to　sell　the　disturbed　data　to　the　data　consumer　(DC),　where　the　disturbance　parameter　and　the　data　price　are　negotiated　by　them,　and　DO　independently　adds　the　disturbance　noise　to　data　(usually　continuous　type)　following　the　negotiation　result.　However,　DOs　may　violate　the　negotiated　parameter　and　add　more　noise　to　data　while　obtaining　the　negotiated　price,　which　damages　DC's　disturbed　data　availability.　This　deficiency　is　rooted　in　the　absence　of　supervision　and　verifiability　on　DOs'　independent　disturbances.　Aiming　at　the　above　problem,　we　devise　a　privacy-preserved　data　trading　via　verifiable　data　disturbance.　Specifically,　the　honest-but-curious　disturbance　server　(DS)　is　introduced　to　generate　encrypted　verifiable　disturbance　noises,　and　secretly　distribute　noises　to　DOs　referring　to　the　method　of　private　information　retrieval.　Using　homomorphic　encryption,　DOs　finish　data　disturbance　without　knowing　noises'　specific　sizes.　Subsequently,　DC　selects　DOs　to　verify　with　our　proposed　anti-forgery　verification,　where　the　anti-forgery　on　both　disturbance　noise　and　original　data　guarantees　verification　correctness.　Theoretical　analysis　proves　that　DOs'　original　data　is　preserved　in　data　trading.　Extensive　experiments　using　the　real-world　dataset　demonstrate　that　our　scheme　can　detect　more　than　80%　of　malicious　DOs　and　decrease　their　utilities　to　punish　malicious　disturbance　compared　with　existing　works.　IEEE