Sensors　have　been　deployed　into　different　scenarios　to　collect　data,　including　health　data,　environmental　data,　etc.　Data　have　been　collected,　transmitted,　analyzed,　etc.　Those　data　are　highly　related　to　people＇s　privacy,　protecting　data　privacy　becomes　necessary.　Different　methods　have　been　applied　to　protect　data　privacy　during　the　life　cycle　in　the　Internet　of　Things　scenarios.　At　the　data　collecting　phase,　data　aggregation　methods　are　proposed.　At　the　data　transmission　phase,　mutual　authentication　and　key　establishment　schemes　are　proposed　to　help　entities　to　build　a　secure　two-way　communication　channel,　data　can　be　transmitted　securely.　At　the　data　analyzing　phase,　privacy-preserving　machine　learning　methods　have　been　discussed,　including　collaboratively　learning　and　other　encrypted　machine　learning　as　a　service　technology,　they　can　protect　users＇　data　privacy　at　the　training　phase　and　inference　phase　respectively.　In　this　study,　we　mainly　discussed　these　kinds　of　methods　for　protecting　data　security　and　privacy　in　the　Internet　of　Things　scenario.　©　2021　IEEE.