This　paper　considers　the　minimax　estimation　of　the　high-dimensional　sparse　covariance　matrices　in　the　presence　of　missing　observations.　Based　on　random　missing　data,　the　upper　bounds　of　convergence　rate　about　the　data-driven　thresholding　estimator　are　constructed　over　a　large　class　of　sparse　covariance　matrices　under　the　l(1)　norm　and　the　Frobenius　norm.　In　addition,　we　use　Le　Cam＇s　lemma　and　the　relation　between　the　total　variation　affinity　and　the　Kullback-Leibler　divergence　to　establish　the　lower　bounds　which　illustrate　the　desired　upper　bounds　cannot　be　improved.　It　is　worth　mentioning　that　the　approach　we　adopt　to　get　the　lower　bounds　is　simpler　than　the　existing　ones.