The　rapidly　exploding　of　user　data,　especially　applications　of　neural　networks,　involves　analyzing　data　collected　from　individuals,　which　brings　convenience　to　life.　Meanwhile,　privacy　leakage　in　the　applications　as　a　potential　threat　needs　to　be　addressed　urgently.　However,　removing　private　information　from　models　is　difficult　once　the　user＇s　sensitive　data　enters　machine　learning　models,　particularly　neural　networks.　Most　of　the　previous　amnestic　methods　based　on　retraining　require　full　access　to　the　training　set　of　the　target　model　and　have　limited　improvements　in　computational　resources　and　time　improvement.　In　this　paper,　we　propose　Scrubber,　which　removes　sensitive　data　from　the　original　model　via　influence　estimation　to　produce　an　unlearning　model　that　is　approximately　indistinguishable　from　the　retrained　model.　S　crubber　builds　on　the　essential　concept　of　influence　function　and　reformulates　the　influence　estimation　as　a　closed-form　update　of　forgetting.　For　learned　models　with　strictly　convex　loss　functions,　our　approach　theoretically　guarantees　the　effectiveness　of　forgetting　while　empirically　demonstrating　forgetting　performance.　For　models　with　non-convex　losses,　we　relax　strictly　convex　assumptions　by　applying　a　damping　term　that　allows　us　to　make　approximate　estimates　with　negligible　errors　from　the　original　assumption.　Furthermore,　experiments　show　that　S　crubber　only　causes　less　than　1%　and　3%　accuracy　drop　with　more　than　80%　forgetting　rate　on　average　for　logistic　regression　models　and　convolutional　neural　networks.　The　accuracy　drop　is　reduced　by　2%-3%　compared　to　most　state-of-the-art　methods.