Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (5): 1497-1503.DOI: 10.11772/j.issn.1001-9081.2022040552
Special Issue: 网络空间安全
• Cyber security • Previous Articles Next Articles
Ran ZHAI1,2,3, Xuebin CHEN1,2,3(), Guopeng ZHANG1,2,3, Langtao PEI1,2,3, Zheng MA1,2,3
Received:
2022-04-21
Revised:
2022-08-10
Accepted:
2022-08-18
Online:
2022-09-29
Published:
2023-05-10
Contact:
Xuebin CHEN
About author:
ZHAI Ran, born in 1998, M. S. candidate. Her research interests include data security, network security, privacy protection.Supported by:
翟冉1,2,3, 陈学斌1,2,3(), 张国鹏1,2,3, 裴浪涛1,2,3, 马征1,2,3
通讯作者:
陈学斌
作者简介:
翟冉(1998—),女,河北唐山人,硕士研究生,CCF会员,主要研究方向:数据安全、网络安全、隐私保护基金资助:
CLC Number:
Ran ZHAI, Xuebin CHEN, Guopeng ZHANG, Langtao PEI, Zheng MA. Improved K-anonymity privacy protection algorithm based on different sensitivities[J]. Journal of Computer Applications, 2023, 43(5): 1497-1503.
翟冉, 陈学斌, 张国鹏, 裴浪涛, 马征. 基于不同敏感度的改进K-匿名隐私保护算法[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1497-1503.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022040552
删除属性 | 准确率 | 删除属性 | 准确率 |
---|---|---|---|
age | 0.856 351 | race | 0.862 365 |
workclass | 0.859 832 | sex | 0.863 486 |
fwlght | 0.862 263 | capital-gain | 0.844 114 |
education | 0.863 742 | capital-loss | 0.857 856 |
education-num | 0.861 423 | hours-per-week | 0.860 666 |
marital-status | 0.861 521 | native-country | 0.862 123 |
occupation | 0.860 164 | None | 0.863 824 |
relationship | 0.861 997 |
Tab. 1 Prediction accuracy of random forest on Adult dataset
删除属性 | 准确率 | 删除属性 | 准确率 |
---|---|---|---|
age | 0.856 351 | race | 0.862 365 |
workclass | 0.859 832 | sex | 0.863 486 |
fwlght | 0.862 263 | capital-gain | 0.844 114 |
education | 0.863 742 | capital-loss | 0.857 856 |
education-num | 0.861 423 | hours-per-week | 0.860 666 |
marital-status | 0.861 521 | native-country | 0.862 123 |
occupation | 0.860 164 | None | 0.863 824 |
relationship | 0.861 997 |
删除属性 | 准确率 | 删除属性 | 准确率 |
---|---|---|---|
age | 0.895 578 481 945 4680 | day | 0.892 188 651 436 993 4 |
job | 0.896 389 093 588 798 8 | month | 0.897 494 473 102 431 9 |
marital | 0.895 136 330 140 015 0 | duration | 0.890 272 660 280 029 5 |
education | 0.899 557 848 194 546 9 | campaign | 0.896 536 477 523 949 8 |
default | 0.895 062 638 172 439 3 | pdays | 0.897 347 089 167 280 9 |
balance | 0.896 315 401 621 223 3 | previous | 0.897 715 549 005 158 5 |
housing | 0.897 568 165 070 007 5 | poutcome | 0.881 503 316 138 540 9 |
loan | 0.897 789 240 972 733 9 | None | 0.904 200 442 151 805 5 |
contact | 0.892 851 879 145 173 2 |
Tab. 2 Prediction accuracy of RF on Bank Marketing dataset
删除属性 | 准确率 | 删除属性 | 准确率 |
---|---|---|---|
age | 0.895 578 481 945 4680 | day | 0.892 188 651 436 993 4 |
job | 0.896 389 093 588 798 8 | month | 0.897 494 473 102 431 9 |
marital | 0.895 136 330 140 015 0 | duration | 0.890 272 660 280 029 5 |
education | 0.899 557 848 194 546 9 | campaign | 0.896 536 477 523 949 8 |
default | 0.895 062 638 172 439 3 | pdays | 0.897 347 089 167 280 9 |
balance | 0.896 315 401 621 223 3 | previous | 0.897 715 549 005 158 5 |
housing | 0.897 568 165 070 007 5 | poutcome | 0.881 503 316 138 540 9 |
loan | 0.897 789 240 972 733 9 | None | 0.904 200 442 151 805 5 |
contact | 0.892 851 879 145 173 2 |
删除属性 | 准确率差值 | 删除属性 | 准确率差值 |
---|---|---|---|
age | 0.007 472 | relationship | 0.001 827 |
workclass | 0.003 992 | race | 0.001 458 |
fwlght | 0.001 561 | sex | 0.000 338 |
education | 0.000 082 | capital-gain | 0.019 710 |
education-num | 0.002 400 | capital-loss | 0.005 968 |
marital-status | 0.002 303 | hours-per-week | 0.003 158 |
occupation | 0.003 659 | native-country | 0.001 701 |
Tab. 3 Difference in prediction accuracy for eliminating each attribute or not in Adult dataset
删除属性 | 准确率差值 | 删除属性 | 准确率差值 |
---|---|---|---|
age | 0.007 472 | relationship | 0.001 827 |
workclass | 0.003 992 | race | 0.001 458 |
fwlght | 0.001 561 | sex | 0.000 338 |
education | 0.000 082 | capital-gain | 0.019 710 |
education-num | 0.002 400 | capital-loss | 0.005 968 |
marital-status | 0.002 303 | hours-per-week | 0.003 158 |
occupation | 0.003 659 | native-country | 0.001 701 |
删除属性 | 准确率差值 |
---|---|
age | 0.008 621 960 206 337 542 0 |
job | 0.007 811 348 563 006 692 6 |
marital | 0.009 064 112 011 790 470 0 |
education | 0.004 642 593 957 258 634 0 |
default | 0.009 137 803 979 366 255 0 |
balance | 0.007 885 040 530 582 255 0 |
housing | 0.006 632 277 081 798 032 5 |
loan | 0.006 411 201 179 071 568 0 |
contact | 0.011 348 563 006 632 340 0 |
day | 0.012 011 790 714 812 065 0 |
month | 0.006 705 969 049 373 595 0 |
duration | 0.013 927 781 871 776 013 0 |
campaign | 0.007 663 964 627 855 679 4 |
pdays | 0.006 853 352 984 524 608 0 |
previous | 0.006 484 893 146 647 019 4 |
poutcome | 0.022 697 126 013 264 568 0 |
Tab. 4 Difference in prediction accuracy for eliminating each attribute or not in Bank Marketing dataset
删除属性 | 准确率差值 |
---|---|
age | 0.008 621 960 206 337 542 0 |
job | 0.007 811 348 563 006 692 6 |
marital | 0.009 064 112 011 790 470 0 |
education | 0.004 642 593 957 258 634 0 |
default | 0.009 137 803 979 366 255 0 |
balance | 0.007 885 040 530 582 255 0 |
housing | 0.006 632 277 081 798 032 5 |
loan | 0.006 411 201 179 071 568 0 |
contact | 0.011 348 563 006 632 340 0 |
day | 0.012 011 790 714 812 065 0 |
month | 0.006 705 969 049 373 595 0 |
duration | 0.013 927 781 871 776 013 0 |
campaign | 0.007 663 964 627 855 679 4 |
pdays | 0.006 853 352 984 524 608 0 |
previous | 0.006 484 893 146 647 019 4 |
poutcome | 0.022 697 126 013 264 568 0 |
敏感程度集群 | 属性 | 准确率差值 |
---|---|---|
第一集群 | capital-gain | 0.019 710 |
第二集群 | age | 0.007 472 |
capital-loss | 0.005 968 | |
第三集群 | workclass | 0.003 992 |
occupation | 0.003 659 | |
hours-per-week | 0.003 158 | |
education-num | 0.002 400 | |
第四集群 | marital-status | 0.002 303 |
relationship | 0.001 827 | |
native-country | 0.001 701 | |
fwlght | 0.001 561 | |
race | 0.001 458 | |
第五集群 | sex | 0.000 338 |
education | 0.000 082 |
Tab. 5 k-means clustering results on Adult dataset
敏感程度集群 | 属性 | 准确率差值 |
---|---|---|
第一集群 | capital-gain | 0.019 710 |
第二集群 | age | 0.007 472 |
capital-loss | 0.005 968 | |
第三集群 | workclass | 0.003 992 |
occupation | 0.003 659 | |
hours-per-week | 0.003 158 | |
education-num | 0.002 400 | |
第四集群 | marital-status | 0.002 303 |
relationship | 0.001 827 | |
native-country | 0.001 701 | |
fwlght | 0.001 561 | |
race | 0.001 458 | |
第五集群 | sex | 0.000 338 |
education | 0.000 082 |
敏感程度集群 | 属性 | 准确率差值 |
---|---|---|
第一集群 | poutcome | 0.022 697 126 013 264 568 |
第二集群 | duration | 0.013 927 781 871 776 013 |
day | 0.012 011 790 714 812 065 | |
contact | 0.011 348 563 006 632 340 | |
第三集群 | default | 0.009 137 803 979 366 255 |
marital | 0.009 064 112 011 790 470 | |
age | 0.008 621 960 206 337 542 | |
第四集群 | balance | 0.007 885 040 530 582 255 |
job | 0.007 811 348 563 006 693 | |
campaign | 0.007 663 964 627 855 679 | |
pdays | 0.006 853 352 984 524 608 | |
month | 0.006 705 969 049 373 595 | |
housing | 0.006 632 277 081 798 033 | |
previous | 0.006 484 893 146 647 019 | |
loan | 0.006 411 201 179 071 568 | |
第五集群 | education | 0.004 642 593 957 258 634 |
Tab. 6 k-means clustering results on Bank Marketing dataset
敏感程度集群 | 属性 | 准确率差值 |
---|---|---|
第一集群 | poutcome | 0.022 697 126 013 264 568 |
第二集群 | duration | 0.013 927 781 871 776 013 |
day | 0.012 011 790 714 812 065 | |
contact | 0.011 348 563 006 632 340 | |
第三集群 | default | 0.009 137 803 979 366 255 |
marital | 0.009 064 112 011 790 470 | |
age | 0.008 621 960 206 337 542 | |
第四集群 | balance | 0.007 885 040 530 582 255 |
job | 0.007 811 348 563 006 693 | |
campaign | 0.007 663 964 627 855 679 | |
pdays | 0.006 853 352 984 524 608 | |
month | 0.006 705 969 049 373 595 | |
housing | 0.006 632 277 081 798 033 | |
previous | 0.006 484 893 146 647 019 | |
loan | 0.006 411 201 179 071 568 | |
第五集群 | education | 0.004 642 593 957 258 634 |
敏感程度集群 | 属性 | 准确率 差值1 | 准确率 差值2 | 准确率 差值3 |
---|---|---|---|---|
第一集群 | capital-gain | 0.019 9 | 0.020 5 | 0.019 3 |
第二集群 | age | 0.008 2 | 0.008 9 | 0.007 8 |
capital-loss | 0.006 8 | 0.006 7 | 0.007 1 | |
第三集群 | workclass | 0.003 1 | 0.004 3 | 0.003 8 |
occupation | 0.003 3 | 0.003 9 | 0.003 1 | |
hours-per-week | 0.004 2 | 0.002 7 | 0.002 5 | |
education-num | 0.002 9 | 0.002 6 | 0.002 3 | |
第四集群 | marital-status | 0.002 3 | 0.002 4 | 0.001 9 |
relationship | 0.001 7 | 0.001 9 | 0.001 4 | |
native-country | 0.001 8 | 0.001 6 | 0.001 3 | |
fwlght | 0.001 3 | 0.001 8 | 0.001 5 | |
race | 0.001 6 | 0.001 1 | 0.001 7 | |
第五集群 | sex | 0.000 2 | 0.000 3 | 0.000 1 |
education | 0.000 1 | 0.000 1 | 0.000 2 |
Tab. 7 Reliability verification of k-means clustering results on Adult dataset
敏感程度集群 | 属性 | 准确率 差值1 | 准确率 差值2 | 准确率 差值3 |
---|---|---|---|---|
第一集群 | capital-gain | 0.019 9 | 0.020 5 | 0.019 3 |
第二集群 | age | 0.008 2 | 0.008 9 | 0.007 8 |
capital-loss | 0.006 8 | 0.006 7 | 0.007 1 | |
第三集群 | workclass | 0.003 1 | 0.004 3 | 0.003 8 |
occupation | 0.003 3 | 0.003 9 | 0.003 1 | |
hours-per-week | 0.004 2 | 0.002 7 | 0.002 5 | |
education-num | 0.002 9 | 0.002 6 | 0.002 3 | |
第四集群 | marital-status | 0.002 3 | 0.002 4 | 0.001 9 |
relationship | 0.001 7 | 0.001 9 | 0.001 4 | |
native-country | 0.001 8 | 0.001 6 | 0.001 3 | |
fwlght | 0.001 3 | 0.001 8 | 0.001 5 | |
race | 0.001 6 | 0.001 1 | 0.001 7 | |
第五集群 | sex | 0.000 2 | 0.000 3 | 0.000 1 |
education | 0.000 1 | 0.000 1 | 0.000 2 |
敏感程度集群 | 属性 | 准确率 差值1 | 准确率 差值2 | 准确率 差值3 |
---|---|---|---|---|
第一集群 | poutcome | 0.021 3 | 0.022 8 | 0.019 1 |
第二集群 | duration | 0.014 7 | 0.016 2 | 0.014 3 |
day | 0.014 0 | 0.016 2 | 0.012 5 | |
contact | 0.012 8 | 0.014 7 | 0.014 5 | |
第三集群 | default | 0.009 5 | 0.008 1 | 0.008 6 |
marital | 0.009 8 | 0.009 1 | 0.009 2 | |
age | 0.008 8 | 0.008 5 | 0.009 1 | |
第四集群 | balance | 0.007 1 | 0.006 6 | 0.007 3 |
job | 0.007 3 | 0.005 8 | 0.006 6 | |
campaign | 0.006 7 | 0.006 2 | 0.006 9 | |
pdays | 0.007 0 | 0.007 1 | 0.007 1 | |
month | 0.006 5 | 0.005 9 | 0.006 2 | |
housing | 0.006 8 | 0.006 3 | 0.006 8 | |
previous | 0.006 1 | 0.007 0 | 0.006 3 | |
loan | 0.006 3 | 0.005 9 | 0.005 8 | |
第五集群 | education | 0.003 2 | 0.004 2 | 0.002 9 |
Tab. 8 Reliability Verification of k-means clustering results on Bank Marketing dataset
敏感程度集群 | 属性 | 准确率 差值1 | 准确率 差值2 | 准确率 差值3 |
---|---|---|---|---|
第一集群 | poutcome | 0.021 3 | 0.022 8 | 0.019 1 |
第二集群 | duration | 0.014 7 | 0.016 2 | 0.014 3 |
day | 0.014 0 | 0.016 2 | 0.012 5 | |
contact | 0.012 8 | 0.014 7 | 0.014 5 | |
第三集群 | default | 0.009 5 | 0.008 1 | 0.008 6 |
marital | 0.009 8 | 0.009 1 | 0.009 2 | |
age | 0.008 8 | 0.008 5 | 0.009 1 | |
第四集群 | balance | 0.007 1 | 0.006 6 | 0.007 3 |
job | 0.007 3 | 0.005 8 | 0.006 6 | |
campaign | 0.006 7 | 0.006 2 | 0.006 9 | |
pdays | 0.007 0 | 0.007 1 | 0.007 1 | |
month | 0.006 5 | 0.005 9 | 0.006 2 | |
housing | 0.006 8 | 0.006 3 | 0.006 8 | |
previous | 0.006 1 | 0.007 0 | 0.006 3 | |
loan | 0.006 3 | 0.005 9 | 0.005 8 | |
第五集群 | education | 0.003 2 | 0.004 2 | 0.002 9 |
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