Privacy　protection　is　a　fundamental　issue　in　the　era　of　big　data.　For　personalized　privacy　protection,　it　is　necessary　to　measure　the　amount　or　the　degree　of　privacy　leakage.　To　facilitate　such　measurement,　semantic　similarities　and　relationships　of　words　should　be　determined　since　the　words　may　come　from　multiple　sources　and　present　themselves　in　as　many　different　ways　as　one　can　imagine,　an　intrinsic　nature　of　big　data.　WordNet　has　been　widely　used　for　measuring　the　semantic　similarity　of　words.　This　paper　aims　at　analyzing　the　suitability　of　applying　WordNet　to　measuring　the　semantic　similarity　or　relatedness　of　words　in　the　field　of　privacy.　The　analysis　includes　an　experiment　designed　to　obtain　human　rating　scores　as　the　benchmark　dataset　and　a　comprehensive　comparison　between　results　from　four　WordNet　based　measures　and　the　human　rating　scores.　The　conclusion　of　the　analysis　is　that　current　WordNet　based　measures　are　not　very　suitable　for　privacy　measurement.　Therefore,　this　paper　also　provides　some　suggestions　on　possible　ways　of　enhancing　WordNet　to　improve　the　effectiveness　of　semantic　similarity　or　relatedness　measurement　in　the　field　of　privacy　to　support　more　effective　and　personalized　privacy　protection.