Privacy　protection　has　received　a　lot　of　attention　in　recent　years　since　in　the　era　of　big　data,　abundant　information　about　individuals　can　be　easily　acquired.　Meanwhile,　as　a　prerequisite　for　effective　privacy　protection,　the　measurement　of　privacy　disclosure　is　essential.　Although　some　work　has　been　done　on　the　evaluation　of　privacy　disclosure　via　quantification　for　the　protection　of　privacy,　not　much　attention　has　been　placed　on　exploring　the　relationships　between　privacy　information,　resulting　in　underestimation,　if　not　ill-formed　reasoning,　of　privacy　disclosure.　In　this　paper,　we　propose　an　ontology-based　approach　to　measure　privacy　disclosure　by　exploring　the　relationships　between　privacy　information　based　on　the　WordNet.　We　first　propose　an　algorithm　for　deriving　or　measuring　privacy　disclosure　based　on　a　set　of　words　or　concepts　from　text　data　related　to　individuals　to　ensure　that　the　disclosure　of　certain　user　privacy　can　still　be　deduced　and　measured　even　if　the　set　of　words　or　concepts　don＇t　seem　to　be　much　related　to　it.　We　then　perform　a　set　of　experiment　by　applying　the　proposed　algorithm　to　some　public　information　of　ten　public　figures　from　different　walks　of　life　to　evaluate　the　effectiveness　of　the　algorithm　and　to　shed　some　light　on　the　characteristics　of　privacy　disclosure　in　the　real　world　in　the　era　of　big　data.　The　work　can　thus　serve　as　the　foundation　for　the　development　of　mechanisms　for　limiting　or　reducing　privacy　disclosure　to　achieve　better　protection　of　individual　privacy.