The　data　publication　from　multiple　contributors　has　been　long　considered　a　fundamental　task　for　data　processing　in　various　domains.　It　has　been　treated　as　one　prominent　prerequisite　for　enabling　AI　techniques　in　wireless　networks.　With　the　emergence　of　diversified　smart　devices　and　applications,　data　held　by　individuals　becomes　more　pervasive　and　nontrivial　for　publication.　First,　the　data　are　more　private　and　sensitive,　as　they　cover　every　aspect　of　daily　life,　from　the　incoming　data　to　the　fitness　data.　Second,　the　publication　of　such　data　is　also　bandwidth-consuming,　as　they　are　likely　to　be　stored　on　mobile　devices.　The　local　differential　privacy　has　been　considered　a　novel　paradigm　for　such　distributed　data　publication.　However,　existing　works　mostly　request　the　encoding　of　contents　into　vector　space　for　publication,　which　is　still　costly　in　network　resources.　Therefore,　this　work　proposes　a　novel　framework　for　highly　efficient　privacy-preserving　data　publication.　Specifically,　two　sampling-based　algorithms　are　proposed　for　the　histogram　publication,　which　is　an　important　statistic　for　data　analysis.　The　first　algorithm　applies　a　bit-level　sampling　strategy　to　both　reduce　the　overall　bandwidth　and　balance　the　cost　among　contributors.　The　second　algorithm　allows　consumers　to　adjust　their　focus　on　different　intervals　and　can　properly　allocate　the　sampling　ratios　to　optimize　the　overall　performance.　Both　the　analysis　and　the　validation　of　real-world　data　traces　have　demonstrated　the　advancement　of　our　work.　©　2021　Guoming　Lu　et　al.