The　broad　learning　system-based　data　processing　method　has　been　widely　used　in　the　field　of　industrial　intelligent　operation　and　maintenance,　and　has　achieved　impressive　results.　However,　data　island　created　by　privacy　concerns　in　real　industrial　scenarios　makes　centralized　deep　training　extremely　difficult.　In　addition,　the　fluctuating　operating　conditions　of　machines　including　speed　and　load　significantly　affect　the　distribution　of　monitoring　data　representing　the　operating　state　of　the　device.　These　two　problems　make　it　difficult　to　apply　broad　learning　system　to　practical　application　scenarios.　Therefore,　in　view　of　the　above　problems,　a　novel　cross　domain　privacy-preserving　broad　network　(CDPPBN)　for　cross-domain　fault　classification　of　rotating　machinery　is　presented.　Specifically,　a　cross-domain　interactive　data　encryption　protocol　feature　extractor　is　designed　firstly　to　ensure　the　privacy　and　security　of　both　source　and　target　domain　data　during　feature　extraction　process.　Then,　a　domain　adaptation　target　classifier　is　constructed　to　achieve　the　fault　classification　task　in　the　target　domain　without　labels.　In　this　classifier,　a　maximum　mean　discrepancy　regularization　term　is　constructed　to　match　the　mean　projections　of　the　source　and　target　domains　to　adapt　the　source　classifier　to　the　target　domain,　and　a　semi　supervised　local　graph　embedding　regularization　term　is　constructed　to　mine　the　structural　features　of　the　target　domain　data.　CDPPBN　not　only　has　high　performance　in　terms　of　data　privacy　protection,　but　also　makes　full　use　of　both　labeled　source　data　and　unlabeled　target　data　to　generate　a　domain　adaptation　target　classifier.　The　effectiveness　of　the　presented　method　is　verified　through　bearing　monitoring　signal.　Experimental　analysis　shows　that　the　proposed　CDPPBN　has　better　data　privacy　protection　capability　and　data　cross-domain　diagnosis　ability　compared　with　other　methods.