For　high　dimensional　linear　model　with　error-in-variables,　a　novel　debiased　procedure　is　developed　and　analyzed　to　construct　component-wise　confidence　intervals　of　the　regression　coefficient.　The　proposed　method　is　not　only　able　to　account　for　measurement　errors　to　avoid　non-vanishing　biases,　but　also　to　compensate　the　biases　introduced　by　penalization.　The　resulting　estimator　is　asymptotically　unbiased　and　normal　under　mild　conditions.　Then　it　can　be　used　to　construct　valid　confidence　intervals　and　conduct　hypothesis　tests.　Results　of　an　extensive　simulation　study　are　also　presented　to　show　the　efficacy　and　usefulness　of　our　procedure.