With　the　development　of　science　and　computer　technology,　social　networks　are　changing　our　daily　lives.　However,　this　leads　to　new,　often　hidden　dangers　in　areas　such　as　cybersecurity.　Of　these,　the　most　complex　and　harmful　is　the　Advanced　Persistent　Threat　attack　(APT　attack).　The　development　of　personality　analysis　and　prediction　technology　provides　the　APT　attack　a　good　opportunity　to　infiltrate　personality　privacy.　Malicious　people　can　exploit　existing　personality　classifiers　to　attack　social　texts　and　steal　users＇　personal　information.　Therefore,　it　is　of　high　importance　to　hide　personal　privacy　information　in　social　texts.　Based　on　the　personality　privacy　protection　technology　of　adversarial　examples,　we　proposed　a　Supervised　Character　Resemble　Substitution　personality　adversarial　method　(SCRS)　in　this　paper,　which　hides　personality　information　in　social　texts　through　adversarial　examples　to　realize　personality　privacy　protection.　The　adversarial　examples　should　be　capable　of　successfully　disturbing　the　personality　classifier　while　maintaining　the　original　semantics　without　reducing　human　readability.　Therefore,　this　paper　proposes　a　measure　index　of　＂label　contribution＂　to　select　the　words　that　are　important　to　the　label.　At　the　same　time,　in　order　to　maintain　higher　readability,　this　paper　uses　character-level　resemble　substitution　to　generate　adversarial　examples.　Experimental　validation　shows　that　our　method　is　able　to　generate　adversarial　examples　with　good　attack　effect　and　high　readability.