Under given conditions, Gaussian kernel density estimate with minimum integrated square error(ISE) criterion can
be equivalent to the minimum enclosing ball(MEB). Based on this conclusion, a learning method of MEB with privacy cloud
data is proposed, called privacy cloud calibration MEB(PCC-MEB). Meanwhile, PCC-MEB is extended to fuzzy privacy
cloud calibration MEB(FPCC-MEB) by introducing a fuzzy membership function, which can resolve unclassifiable zones
among classes. Experimental results on the artificial and real-word data sets show the effectiveness of presented method.