This　paper　investigates　the　problem　of　sparse　covariance　matrix　estimation　while　the　sampling　set　contains　sensitive　information,　and　both　the　differentially　private　algorithm　and　locally　differentially　private　algorithm　are　adopted　to　preserve　privacy.　It　is　worth　noting　that　the　requirement　of　the　distribution　assumption　in　our　work　is　only　the　existing　bounded　4+e(e　＞　0)　moment.　Meanwhile,　we　reduce　the　error　bounds　by　modifying　the　threshold　of　the　existing　differentially　private　algorithms.　Finally,　the　numerical　simulations　and　results　from　a　real　data　application　are　presented　to　support　our　theoretical　claims.