Edge　intelligence　is　a　critical　enabler　of　intelligent　application　services　in　the　Internet　of　Things　(IoT).　However,　due　to　complex　environmental　factors,　edge　devices　are　subject　to　constant　dynamic　changes,　which　can　result　in　security　threats　and　sensitive　information　leakage.　Therefore,　it　is　essential　to　investigate　data　stream　online　analysis　and　detection　strategies　and　implement　an　online　releasing　mechanism　to　ensure　sensitive　information　is　not　leaked.　Existing　work　rarely　addresses　these　issues　simultaneously　or　has　poor　performance,　which　poses　a　challenge.　To　address　this　challenge,　we　propose　an　intelligent　edge　dual-structure　ensemble　method　(IEDSEM),　consisting　of　three　key　components:　data　preprocessing,　drift　detection　data　analytics　(IEDSEM-DDDA),　and　privacy-preserving　data　releasing　(IEDSEM-PPDR).　Data　preprocessing　is　used　primarily　to　enhance　the　quality　of　data　streams　to　improve　the　performance　of　model　learning.　IEDSEM-DDDA　involves　three　sequential　operations:　dynamic　feature　selection,　model　learning　and　selection,　and　online　model　ensemble　deployment　to　achieve　anomaly　detection　of　online　data　streams.　Meanwhile,　IEDSEM-PPDR　uses　differential　privacy　and　online　optimization　operations　to　achieve　intelligent　hierarchical　protection　of　edge　data.　To　validate　the　performance　of　our　proposed　IEDSEM　method,　we　conducted　two　comprehensive　simulation　experiments　on　real　data　machines,　verifying　the　accuracy　of　the　concept　drift　component　detection　and　the　privacy　optimization　performance　of　the　privacy-preserving　component,　respectively.　Simulation　results　show　that　compared　with　several　other　advanced　high-performance　algorithms,　our　algorithm　has　over　99%　accuracy　in　data　stream　analysis　detection　and　more　outstanding　privacy-preserving　ability.　IEEE