Consistency preserving database watermarking algorithm for decision trees
Database watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data leakage. However, when the watermarked database is used for data mining model building, such as decision trees, it may...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-12-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864822002838 |
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| author | Qianwen Li Xiang Wang Qingqi Pei Xiaohua Chen Kwok-Yan Lam |
| author_facet | Qianwen Li Xiang Wang Qingqi Pei Xiaohua Chen Kwok-Yan Lam |
| author_sort | Qianwen Li |
| collection | DOAJ |
| description | Database watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data leakage. However, when the watermarked database is used for data mining model building, such as decision trees, it may cause a different mining result in comparison with the result from the original database caused by the distortion of watermark embedding. Traditional watermarking algorithms mainly consider the statistical distortion of data, such as the mean square error, but very few consider the effect of the watermark on database mining. Therefore, in this paper, a consistency preserving database watermarking algorithm is proposed for decision trees. First, label classification statistics and label state transfer methods are proposed to adjust the watermarked data so that the model structure of the watermarked decision tree is the same as that of the original decision tree. Then, the splitting values of the decision tree are adjusted according to the defined constraint equations. Finally, the adjusted database can obtain a decision tree consistent with the original decision tree. The experimental results demonstrated that the proposed algorithm does not corrupt the watermarks, and makes the watermarked decision tree consistent with the original decision tree with a small distortion. |
| format | Article |
| id | doaj-art-d69748a1668d483caa3f1b45d7fa977a |
| institution | Kabale University |
| issn | 2352-8648 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-d69748a1668d483caa3f1b45d7fa977a2024-12-29T04:47:32ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482024-12-0110618511863Consistency preserving database watermarking algorithm for decision treesQianwen Li0Xiang Wang1Qingqi Pei2Xiaohua Chen3Kwok-Yan Lam4School of Telecommunications Engineering, Xidian University, Xi'an, 710071, ChinaSchool of Cyber Engineering, Xidian University, Xi'an, 710071, China; Corresponding author.School of Telecommunications Engineering, Xidian University, Xi'an, China2012 Laboratories, Huawei Technologies, Huawei Technology Co., Ltd, Hangzhou, 310000, ChinaThe School of Computer Science and Engineering, Nanyang Technological University, 639798, SingaporeDatabase watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data leakage. However, when the watermarked database is used for data mining model building, such as decision trees, it may cause a different mining result in comparison with the result from the original database caused by the distortion of watermark embedding. Traditional watermarking algorithms mainly consider the statistical distortion of data, such as the mean square error, but very few consider the effect of the watermark on database mining. Therefore, in this paper, a consistency preserving database watermarking algorithm is proposed for decision trees. First, label classification statistics and label state transfer methods are proposed to adjust the watermarked data so that the model structure of the watermarked decision tree is the same as that of the original decision tree. Then, the splitting values of the decision tree are adjusted according to the defined constraint equations. Finally, the adjusted database can obtain a decision tree consistent with the original decision tree. The experimental results demonstrated that the proposed algorithm does not corrupt the watermarks, and makes the watermarked decision tree consistent with the original decision tree with a small distortion.http://www.sciencedirect.com/science/article/pii/S2352864822002838Consistency preservingDecision treeDatabase watermarkingData mining |
| spellingShingle | Qianwen Li Xiang Wang Qingqi Pei Xiaohua Chen Kwok-Yan Lam Consistency preserving database watermarking algorithm for decision trees Digital Communications and Networks Consistency preserving Decision tree Database watermarking Data mining |
| title | Consistency preserving database watermarking algorithm for decision trees |
| title_full | Consistency preserving database watermarking algorithm for decision trees |
| title_fullStr | Consistency preserving database watermarking algorithm for decision trees |
| title_full_unstemmed | Consistency preserving database watermarking algorithm for decision trees |
| title_short | Consistency preserving database watermarking algorithm for decision trees |
| title_sort | consistency preserving database watermarking algorithm for decision trees |
| topic | Consistency preserving Decision tree Database watermarking Data mining |
| url | http://www.sciencedirect.com/science/article/pii/S2352864822002838 |
| work_keys_str_mv | AT qianwenli consistencypreservingdatabasewatermarkingalgorithmfordecisiontrees AT xiangwang consistencypreservingdatabasewatermarkingalgorithmfordecisiontrees AT qingqipei consistencypreservingdatabasewatermarkingalgorithmfordecisiontrees AT xiaohuachen consistencypreservingdatabasewatermarkingalgorithmfordecisiontrees AT kwokyanlam consistencypreservingdatabasewatermarkingalgorithmfordecisiontrees |