Deep　Learning　(DL)　algorithms　based　on　artificial　neural　networks　have　achieved　remarkable　success　and　are　being　extensively　applied　in　a　variety　of　application　domains,　ranging　from　image　classification,　automatic　driving,　natural　language　processing　to　medical　diagnosis,　credit　risk　assessment,　intrusion　detection.　However,　the　privacy　and　security　issues　of　DL　have　been　revealed　that　the　DL　model　can　be　stolen　or　reverse　engineered,　sensitive　training　data　can　be　inferred,　even　a　recognizable　face　image　of　the　victim　can　be　recovered.　Besides,　the　recent　works　have　found　that　the　DL　model　is　vulnerable　to　adversarial　examples　perturbed　by　imperceptible　noised,　which　can　lead　the　DL　model　to　predict　wrongly　with　high　confidence.　In　this　paper,　we　first　briefly　introduces　the　four　types　of　attacks　and　privacy-preserving　techniques　in　DL.　We　then　review　and　summarize　the　attack　and　defense　methods　associated　with　DL　privacy　and　security　in　recent　years.　To　demonstrate　that　security　threats　really　exist　in　the　real　world,　we　also　reviewed　the　adversarial　attacks　under　the　physical　condition.　Finally,　we　discuss　current　challenges　and　open　problems　regarding　privacy　and　security　issues　in　DL.