Download pdf
AI-driven anonymization: Protecting personal data privacy while leveraging machine learning
- 1 Computer Information Science, Sam Houston State University, Huntsville, TX, USA
- 2 Master of Science in Computer Science, San Fransisco Bay University, Fremont CA, USA
- 3 Accounting, Sun Yat-Sen University, HongKong
- 4 Department of Economics, University of Chicago, Chicago, IL, USA
- 5 Bachelor of Engineering, Guangdong University of Technology, ShenZhen, CN
* Author to whom correspondence should be addressed.
Abstract
The development of artificial intelligence has significantly transformed people's lives. However, it has also posed a significant threat to privacy and security, with numerous instances of personal information being exposed online and reports of criminal attacks and theft. Consequently, the need to achieve intelligent protection of personal information through machine learning algorithms has become a paramount concern. Artificial intelligence leverages advanced algorithms and technologies to effectively encrypt and anonymize personal data, enabling valuable data analysis and utilization while safeguarding privacy. This paper focuses on personal data privacy protection and the promotion of anonymity as its core research objectives. It achieves personal data privacy protection and detection through the use of machine learning's differential privacy protection algorithm. The paper also addresses existing challenges in machine learning related to privacy and personal data protection, offers improvement suggestions, and analyzes factors impacting datasets to enable timely personal data privacy detection and protection.
Keywords
Machine learning, Differential privacy algorithm, Personal data protection, Drive anonymization
[1]. Shokri, Reza, and Vitaly Shmatikov. "Privacy-preserving deep learning." Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 2015.
[2]. Rivest R，Adleman L， Dertouzos M L. On data banks and privacy homomorphisms [J]. Foundations of Secure Computation，1978，4(11): 169180.
[3]. Gentry C. Fully homomorphic encryption using ideal lattices[C] //Proc of the 4lst Annual ACM Symp on Theory of Computing. New York: ACM，2009: 169178Dwork C，McSherry F, Nissim K, et al. Calibrating noise tosensitivity in private data analysis [C] //Proc of the 3rdTheory of Cryptography Conf. Berlin: Springer， 2006 : 265284
[4]. Atul Adya, Paramvir Bahl, Jitendra Padhye, Alec Wolman, and Lidong Zhou. 2004. A multi-radio unification protocol for IEEE 802.11 wireless networks. In Proceedings of the IEEE 1st International Conference on Broadnets Networks (BroadNets’04) . IEEE, Los Alamitos, CA, 210–217. https://doi.org/10.1109/BROADNETS.2004.8.
[5]. Xinyu Zhao, et al. “Effective Combination of 3D-DenseNet’s Artificial Intelligence Technology and Gallbladder Cancer Diagnosis Model”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 81-84, https://doi.org/10.54097/iMKyFavE.
[6]. Shulin Li, et al. “Application Analysis of AI Technology Combined With Spiral CT Scanning in Early Lung Cancer Screening”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 52-55, https://doi.org/10.54097/LAwfJzEA.
[7]. Liu, Bo & Zhao, Xinyu & Hu, Hao & Lin, Qunwei & Huang, Jiaxin. (2023). Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN. Journal of Theory and Practice of Engineering Science. 3. 36-42. 10.53469/jtpes.2023.03(12).06.
[8]. Yu, Liqiang, et al. “Research on Machine Learning With Algorithms and Development”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 7-14, doi:10.53469/jtpes.2023.03(12).02.
[9]. Xin, Q., He, Y., Pan, Y., Wang, Y., & Du, S. (2023). The implementation of an AI-driven advertising push system based on a NLP algorithm. International Journal of Computer Science and Information Technology, 1(1), 30-37.0
[10]. Zhou, H., Lou, Y., Xiong, J., Wang, Y., & Liu, Y. (2023). Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery. Frontiers in Computing and Intelligent Systems, 6(1), 103-106.6
[11]. Implementation of an AI-based MRD Evaluation and Prediction Model for Multiple Myeloma. (2024). Frontiers in Computing and Intelligent Systems, 6(3), 127-131. https://doi.org/10.54097/zJ4MnbWW.
[12]. Zhang, Q., Cai, G., Cai, M., Qian, J., & Song, T. (2023). Deep Learning Model Aids Breast Cancer Detection. Frontiers in Computing and Intelligent Systems, 6(1), 99-102.3
[13]. Manchini Carlos, et al. "A new approach to data differential privacy based on regression models under heteroscedasticity with applications to machine learning repository data." Information Sciences 627. (2023):
[14]. Xu, J., Pan, L., Zeng, Q., Sun, W., & Wan, W. (2023). Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks. Frontiers in Computing and Intelligent Systems, 6(1), 66-69.7
Cite this article
Yang,L.;Tian,M.;Xin,D.;Cheng,Q.;Zheng,J. (2024). AI-driven anonymization: Protecting personal data privacy while leveraging machine learning. Applied and Computational Engineering,67,273-279.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).