Intelligent　equipment　within　the　Internet　of　things　(IoT)　carries　out　massive,　frequent,　and　persistent　data　communication,　making　privacy　protection　particularly　critical.　Different　from　regular　encryption　methods　or　neural　networks,　this　study　proposes　a　de-correlation　neural　network　(DeCNN)　which　synchronously　realizes　the　estimation　and　privacy　protection　by　a　comprehensive　loss　function.　In　addition,　a　two-stage　learning　algorithm　is　utilized　for　solution　optimization　and　computation　enhancement.　The　DeCNN　is　deployed　in　the　deep　fingerprint　positioning,　and　the　experimental　results　demonstrate　that　the　proposed　method　decreases　the　maximal　correlation　coefficient　between　the　transmission　data　and　target　data　from　0.95　to　0.34　(and　0.13)　when　the　positioning　error　reaches　1.31　m　(and　2.88　m).　©　1997-2012　IEEE.