Privacy　protection　in　online　social　networks　(OSNs)　has　received　a　great　deal　of　attention　in　recent　years.　One　way　of　circumventing　conventional　privacy　protection　is　privacy　inference　based　on　data　that　can　be　easily　obtained　in　OSNs.　Previous　work　on　privacy　inference　has　studied　the　issue　mostly　from　the　viewpoint　of　the　attackers　and　methods　thus　designed　were　mostly　aimed　at　pursuing　the　accuracy　of　the　inference　results　with　little　regard　on　the　causes　of　privacy　breaches.　To　develop　more　effective　privacy　protection　mechanisms　that　takes　privacy　inference　into　consideration,　it　is　necessary　to　identify　the　information　that　plays　a　more　important　role　in　privacy　breaches.　In　this　paper,　we　propose　a　privacy-sensitive　data　identification　model　in　OSNs,　which　can　identify　key　pieces　of　data　that　are　most　sensitive　as　far　the　privacy　of　the　user　is　concerned.　Firstly,　a　privacy　inference　method　is　proposed　based　on　conditional　random　fields　to　infer　the　privacy　of　the　target　users.　Then,　a　privacy-sensitive　data　identification　method　is　proposed　by　using　the　intermediate　data　of　the　proposed　privacy　inference　method　based　on　the　targeted　influence　maximization　algorithm.　Thus,　the　key　pieces　of　data　in　the　form　of　user　attributes　and　relationships　on　which　the　privacy　of　the　target　user　depends　can　be　determined　to　facilitate　the　implementation　of　privacy　protection　mechanisms.　The　effectiveness　as　well　as　the　advantages　of　the　proposed　model　is　verified　and　demonstrated　through　experiment　using　real　datasets.　The　impact　of　the　key　factors　on　privacy　inference　is　also　analyzed　to　guide　the　design　of　effective　privacy　protection　strategies.