Generally,　as　the　attribute　dimension　of　the　data　set　increases,　the　time　cost　and　noise　interference　generated　by　the　differential　privacy　publishing　method　of　high-dimensional　data　will　also　increase.　Especially　for　high-dimensional　binary　data,　it　is　easy　to　be　covered　by　excessive　noise.　Therefore,　an　efficient　and　low-noise　publishing　method　PrivSCBN(differentially　private　spectral　clustering　Bayesian　network)　is　proposed　for　the　issue　of　privacy　publishing　of　high-dimensional　binary　data.　Firstly,　based　on　Jaccard　distance,　this　method　uses　a　spectral　clustering　algorithm　which　satisfies　differential　privacy　to　divide　the　attributes　set,　and　further　segments　the　original　data　set,　so　as　to　achieve　dimension　reduction.　Secondly,　based　on　the　idea　of　dynamic　programming　and　combined　with　the　exponential　mechanism,　this　method　uses　a　fast　building　Bayesian　network　algorithm　which　satisfies　differential　privacy　to　construct　Bayesian　network　for　each　subset　after　segmentation.　Finally,　this　method　uses　the　value　characteristic　of　conditional　probability　on　binary　data　to　add　noise　to　conditional　distribution　extracted　from　Bayesian　network,　and　reduces　the　noise　by　controlling　the　maximum　in-degrees　of　Bayesian　network.　The　efficiency　and　availability　of　the　PrivSCBN　method　are　verified　by　experiments　on　three　real　high-dimensional　binary　data　sets.　©　2022,　Science　Press.　All　right　reserved.