一种基于图神经网络的地质钻孔数据保护方案

尚浩; 朱恒华; 李双; 宋晓媚; 夏雨; 刘惠; 杨帆

doi:10.3799/dqkx.2021.232

Volume 48 Issue 8

Aug. 2023

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2023 > 48(8): 3151-3161

Shang Hao, Zhu Henghua, Li Shuang, Song Xiaomei, Xia Yu, Liu Hui, Yang Fan, 2023. A Geological Borehole Data Protection Based on Graph Neural Networks. Earth Science, 48(8): 3151-3161. doi: 10.3799/dqkx.2021.232

Citation:

Shang Hao, Zhu Henghua, Li Shuang, Song Xiaomei, Xia Yu, Liu Hui, Yang Fan, 2023. A Geological Borehole Data Protection Based on Graph Neural Networks. Earth Science, 48(8): 3151-3161. doi: 10.3799/dqkx.2021.232

Citation:

PDF( 3756 KB)

A Geological Borehole Data Protection Based on Graph Neural Networks

doi: 10.3799/dqkx.2021.232

Shang Hao^{1
,
,},
Zhu Henghua^{1, 2},
Li Shuang¹,
Song Xiaomei³,
Xia Yu⁴,
Liu Hui⁴,
Yang Fan⁴

1.
Shandong Institute of Geological Survey, Jinan 250014, China
2.
School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
3.
Shandong Provicial Archives of Natural Resources, Jinan 25001, China
4.
School of Computer Science, China University of Geosciences, Wuhan 430074, China

Received Date: 2021-11-23
Publish Date: 2023-08-25

Abstract

Abstract

With the development of deep learning technology, attackers can obtain potentially sensitive information from public geological data through classification, prediction, and other methods, which could lead to the leakage of important geological data. To solve the above problems, we propose a geological drilling data protection model based on graph adversarial attack Gcntack.Based on the degree properties of geological data topology, we first generate attacks that satisfy the same power-law distribution as tiny node disturbance. It can ensure that the adversarial attacks are not easy to be found, and while can change the classification result of the target node. Secondly, we introduce an attention mechanism. Using a graph attention network model based on interpretability, we analyze the properties of key nodes that directly affect the results of the adversarial attacks, so as to verify the rationality of the selecting adversarial nodes in the Gcntack model. Finally, a comprehensive evaluation, based on the benchmark dataset and geological drilling dataset, is presented to show this proposed scheme can reduce the prediction accuracy of attackers and achieve the purpose of protecting important geological drilling data.
- graph neural network,
- graph attention network,
- figure adversarial attack,
- interpretability,
- data protection for geological drilling data,
- deep learning

FullText(HTML)

References(36)

References

Ali-Ozkan, O., Ouda, A., 2019. Key-Based Reversible Data Masking for Business Intelligence Healthcare Analytics Platforms. 2019 International Symposium on Networks, Computers and Communications(ISNCC), 2019: 1-6. https://doi.org/10.1109/ISNCC.2019.8909125.

Bojchevski, A., Günnemann, S., 2019. Adversarial Attacks on Node Embeddings via Graph Poisoning. ICML, 97: 695-704.

Borgs, C., Chayes, J., Cohn, H., et al., 2019. An Theory of Sparse Graph Convergence I: Limits, Sparse Random Graph Models, and Power Law Distributions. Transactions of the American Mathematical Society, 372(5): 3019-3062. https://doi.org/10.1090/tran/7543

Cai, H. Y., Zheng, V. W., Chang, K. C. C., 2018. A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications. IEEE Transactions on Knowledge and Data Engineering, 30(9): 1616-1637. https://doi.org/10.1109/tkde.2018.2807452

Chen, J. Y., Lin, X., Shi, Z. Q., et al., 2020. Link Prediction Adversarial Attack Via Iterative Gradient Attack. IEEE Transactions on Computational Social Systems, 7(4): 1081-1094. https://doi.org/10.1109/tcss.2020.3004059

Cuzzocrea, A., Shahriar, H., 2017. Data Masking Techniques for NoSQL Database Security: A Systematic Review. 2017 IEEE International Conference on Big Data, 2017: 4467-4473. https://doi.org/10.1109/BigData.2017.8258486

Dai, H.J., Li, H., Tian, T., et al., 2018. Adversarial Attack on Graph Structured Data. ICML, 80: 1123-1132.

Eikmeier, N., Gleich, D.F., 2017. Revisiting Power-Law Distributions in Spectra of Real-World Networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017: 817-826.

Gagula, A.C., Santillan, J.R., 2020. Integrating Geographic Information System, Remote Sensing Data, Field Surveys, and Hydraulic Simulations in Irrigation System Evaluation. Proceedings of the 2020 IEEE REGION 10 CONFERENCE (TENCON), Osaka, 2020: 626-630.

Hui, Y.U., Wei, Z., Xinnian, M.A., 2017. A Reversible Decryption Model for Vector and Raster Integration Based on Trigonometric Function. Bulletin of Surveying and Mapping, (10): 89-94.

Jiang, D.H., Zhou, W., 2018. Decryption Model for Vector Geographic Data Based on Chebyshev Polynomials. Journal of Geomatics Science and Technology. 35(3): 321-325(in Chinese with English abstract).

Kipf, T.N., Welling, M., 2017. Semi-Supervised Classification with Graph Convolutional Networks. 5th International Conference on Learning Representations, 1-14.

Li, A. B., Zhu, A. X., 2019. Copyright Authentication of Digital Vector Maps Based on Spatial Autocorrelation Indices. Earth Science Informatics, 12(4): 629-639. https://doi.org/10.1007/s12145-019-00386-z

Li, Y. F., Jin, R., Luo, Y., 2019. Classifying Relations in Clinical Narratives Using Segment Graph Convolutional and Recurrent Neural Networks (Seg-GCRNs). Journal of the American Medical Informatics Association, 26(3): 262-268. https://doi.org/10.1093/jamia/ocy157

Li, H., Zhu, H.H., Hua, W.H., et al., 2020. Key Technologies and Methods for Vector Geographic Data Security Protection. Earth Science, 45(12): 4574-4588 (in Chinese with English abstract).

Liu, H., Zhao, B., Guo, J.B., et al., 2021. Survey on Adversarial Attacks Towards Deep Learning. Journal of Cryptologic Research, 8(2): 202-214(in Chinese with English abstract).

Ma, J.Q., Chang, B., Zhang, X., et al., 2020. CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks. International Conference on Learning Representations, 2020: 1-13.

Marti, R., Li, Z. C., Catry, T., et al., 2020. A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires. Remote Sensing, 12(6): 932. https://doi.org/10.3390/rs12060932

Peng, Y. W., Lan, H., Yue, M. L., et al., 2018. Multipurpose Watermarking for Vector Map Protection and Authentication. Multimedia Tools and Applications, 77(6): 7239-7259. https://doi.org/10.1007/s11042-017-4631-z

Pham, G.N., Ngo, S.T., Bui, A.N., et al., 2019. Vector Map Random Encryption Algorithm Based on Multi-Scale Simplification and Gaussian Distribution. Applied Sciences, 9(22): 4889. https://doi.org/10.3390/app 9224889 doi: 10.3390/app9224889

Qiu, Y. G., Duan, H. T., Sun, J. Y., et al., 2019. Rich-Information Reversible Watermarking Scheme of Vector Maps. Multimedia Tools and Applications, 78(17): 24955-24977. https://doi.org/10.1007/s11042-019-7681-6

Qiu, Y. G., Gu, H. H., Sun, J. Y., 2017. High-Payload Reversible Watermarking Scheme of Vector Maps. Multimedia Tools and Applications, 77(5): 6385-6403. https://doi.org/10.1007/s11042-017-4546-8

Ren, N., Zhu, C. Q., Tong, D. Y., et al., 2020. Commutative Encryption and Watermarking Algorithm Based on Feature Invariants for Secure Vector Map. IEEE Access, 8: 221481-221493. https://doi.org/10.1109/access.2020.3043450

Sun, Y., Wang, S., Tang, X., et al., 2020. Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. WWW '20: The Web Conference 2020, 2020: 673-683.

Van, B.N., Lee, S.H., Kwon, K.R., 2017. Selective Encryption Algorithm Using Hybrid Transform for GIS Vector Map. Journal of Information Processing Systems, 13(1): 68-82. https://doi.org/10.3745/jips.03.0059

Vybornova, Y., Vladislav, S., 2019. Method for Vector Map Protection Based on Using of a Watermark Image as a Secondary Carrier. Proceedings of the ICETE (2). Prague, Czech Republic, 2019: 284-293.

Wang, X.D., Liu, Z., Wang, N.N., et al., 2020. Relational Metric Learning with Dual Graph Attention Networks for Social Recommendation. PAKDD (1) 2020: 104-117.

Xia, D., Ge, Y.F., Tang, H.M., et al., 2020. Segmentation of Region of Interest and Identification of Rock Discontinuities in Digital Borehole Images. Earth Science, 45(11): 4207-4217(in Chinese with English abstract).

Zhai, M.G., Yang, S.F., Chen, N.H., et al., 2018. Big Data Epoch: Challenges and Opportunities for Geology. Bulletin of Chinese Academy of Sciences, 33(8): 825-831(in Chinese with English abstract).

Zhu, D.Y., Zhang, Z.W., Cui, P., et al., 2019. Robust Graph Convolutional Networks Against Adversarial Attacks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 1399-1407.

Zügner, D., Akbarnejad, A., Günnemann, S., 2018. Adversarial Attacks on Neural Networks for Graph Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018: 2847-2856.

江栋华, 周卫, 2018. 一种基于Chebyshev多项式的矢量数据几何精度脱密模型. 测绘科学技术学报, 35(3): 321-325. https://www.cnki.com.cn/Article/CJFDTOTAL-JFJC201803019.htm

李虎, 朱恒华, 花卫华, 等, 2020. 矢量地理数据安全保护关键技术和方法. 地球科学, 45(12): 4574-4588. doi: 10.3799/dqkx.2020.299

刘会, 赵波, 郭嘉宝, 等, 2021. 针对深度学习的对抗攻击综述. 密码学报, 8(2): 202-214. https://www.cnki.com.cn/Article/CJFDTOTAL-MMXB202102002.htm

夏丁, 葛云峰, 唐辉明, 等, 2020. 数字钻孔图像兴趣区域分割与岩体结构面特征识别. 地球科学, 45(11): 4207-4217. doi: 10.3799/dqkx.2020.003

翟明国, 杨树锋, 陈宁华, 等, 2018. 大数据时代: 地质学的挑战与机遇. 中国科学院院刊, 33(8): 825-831. https://www.cnki.com.cn/Article/CJFDTOTAL-KYYX201808012.htm

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(9) / Tables(2)

Get Citation

PDF

XML

Article views (568) PDF downloads(45)

A Geological Borehole Data Protection Based on Graph Neural Networks

doi: 10.3799/dqkx.2021.232

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content