Research Article

Differentially Private Kernel Support Vector Machines Based on the Exponential and Laplace Hybrid Mechanism

Table 4

The performance of the DPKSVMEL algorithm on dataset Heart.

kɛSimilarityAccuracyAUC
MeanStdMaxMinMeanStdMaxMin

20.10.580.002.5584.8177.040.88630.01840.90620.8500
0.679.852.5784.0776.670.89270.01630.90820.8537
0.781.931.4685.1980.000.89320.01680.92090.8614
0.884.331.1585.9381.850.91040.00820.92360.9017
0.985.521.0586.6783.700.92140.01120.93470.9026

20.50.582.812.0185.1978.520.90190.01540.92320.8753
0.684.191.0586.3082.960.90730.00730.91590.8957
0.784.261.0185.9382.590.91330.00700.92230.9017
0.884.632.1387.0479.630.91760.00700.92860.9038
0.986.190.7687.0484.810.92780.00200.93150.9247

210.585.371.0987.0483.700.91440.00590.92230.9054
0.685.001.0587.0483.330.92030.00510.92920.9135
0.785.440.7686.6784.440.92270.00540.93170.9152
0.886.000.8387.0484.810.92540.00350.93270.9213
0.986.370.7287.4184.810.92930.00540.93860.9198

30.10.577.564.9284.0770.370.88940.00960.91080.8773
0.679.562.6283.7076.300.88750.01820.90890.8516
0.782.191.6684.4478.520.90000.01230.91590.8737
0.882.743.6986.6774.810.91180.00570.91900.8987
0.984.631.5586.3081.480.91920.00940.93480.9047

30.50.581.633.7986.3074.440.89050.03900.92030.7887
0.683.441.8685.5679.630.90800.00540.91630.8991
0.784.301.5986.6781.850.91440.00730.92520.9008
0.885.111.5187.7881.850.91980.00390.92940.9160
0.985.701.5488.1582.220.92230.00610.93100.9093

310.584.371.5185.5681.110.91870.00480.92430.9080
0.685.151.7787.0481.110.92120.00460.92540.9091
0.785.671.5788.5282.960.92280.00780.93120.9094
0.886.001.0387.7884.440.92730.00570.93530.9187
0.986.150.8087.0484.810.92790.00360.93540.9229