Privacy　inference　attacks　in　online　social　networks　aim　at　inferring　hidden　attributes　of　target　users　based　on　released　attributes　of　the　users　as　well　as　information　about　social　relationships　in　the　networks.　Although　many　privacy　inference　methods　have　been　proposed　in　recent　years,　most　of　the　methods　have　mostly　focused　on　pursuing　high　accuracy　of　the　inference　results　without　paying　too　much　attention　on　identifying　users　who　make　the　most　contributions　to　the　inference,　making　it　less　useful　in　the　development　of　effective　countermeasures.　In　this　paper,　by　taking　full　consideration　of　the　roles　of　users　in　privacy　inference,　we　propose　a　privacy　inference　method　based　on　graph　attention　networks　that　can　provide　information　about　not　only　the　private　attributes　of　the　target　users,　but　also　the　importance　of　the　users　in　the　process　of　inferring　the　results.　We　also　design　a　novel　mechanism　for　the　selection　of　influential　users　to　make　our　proposed　method　adaptive　to　the　characteristics　of　user　influence　diffusion,　resulting　in　involving　fewer　number　of　users　in　privacy　inference　and　hence　improving　the　efficiency.　We　also　performed　some　experiment　to　evaluate　the　proposed　method　using　a　real　online　social　network　dataset　to　show　the　effectiveness　of　the　proposed　method.　Comparison　with　other　comparable　methods　also　shows　that　the　results　of　our　proposed　method　are　more　accurate.　©　2021　John　Wiley　&　Sons　Ltd.