k-anonymity & Deep Learning
- Dataset used throughout the experiment: Adult dataset (used only 300 samples from the original dataset)
- 我利用 Mondrian 的 k-anonymity 演算法(
K=3 )，我設定的 Quasi-Identifier 為 [ age , education-num , hours-per-week ]，並且將資料集中的 fnlwgt 、 capital-gain 、 capital-loss 去掉，所以 total feature column 為 11 個，要預測的 target 為 income
Model(
(net): Sequential(
(0): Linear(in_features=11, out_features=128, bias=True)
(1): ReLU()
(2): Dropout(p=0.5, inplace=False)
(3): Linear(in_features=128, out_features=64, bias=True)
(4): ReLU()
(5): Linear(in_features=64, out_features=2, bias=True)
(6): Softmax(dim=1)
)
)Dataset without Anonymity
| Category/Metrics |
Precision |
Recall |
| Income <= 50 |
84.62% |
78.57% |
| Income > 50 |
82.35% |
87.50% |
| Category/Metrics |
Precision |
Recall |
| Income <= 50 |
71.43% |
66.67% |
| Income > 50 |
68.75% |
73.33% |
Different privacy level(k value) comparison
| Privacy Level(k value)/Metrics |
Accuracy |
AUC |
| k=2 |
80% |
0.85 |
| k=3 |
70% |
0.76 |
| k=4 |
67% |
0.72 |
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