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View all- Peng SLi HWang WDong KYang MGuo T(2025)LDP-PPA: Local differential privacy protection for principal component analysisInformation Sciences10.1016/j.ins.2025.121962704(121962)Online publication date: Jun-2025
LN-PCA: Differential Privacy Protection of Medical Data Based on Principal Component Analysis Adding Laplace Noise
Pages 697 - 701
Abstract
It is urgent to implement effective privacy protection for medical data. Considering that most of the differential privacy protection schemes directly add noise to the original data, the privacy budget is consumed too quickly when the dataset is very large. Based on the characteristics of large volume and dynamic update of medical data, in this paper we propose a differential privacy protection method for Principal Component Analysis adding Laplace Noise(LN-PCA). Our algorithm has three available forms according to adding noise in different steps N-PCA. We add noise after reducing the dimension of original data, attaching smaller noise added in total. For the ever expanding dataset, the privacy protection of the algorithm for the original data extension has also been proved. Finally, we demonstrate that LN-PCA ensures the validity and availability of the medical data.
References
[1]
Clifton, C., & Tassa, T. (2013). On Syntactic Anonymity and Differential Privacy. 2013 Ieee 29th International Conference on Data Engineering Workshops (Icdew), 88-93. <Go to ISI>://WOS:000326241400018
[2]
Ren, Y. J., Leng, Y., Cheng, Y. P., & Wang, J. (2019). Secure data storage based on blockchain and coding in edge computing. Mathematical Biosciences and Engineering, 16(4), 1874-1892. https://doi.org/10.3934/mbe.2019091
[3]
Ku, D., & Kim, H. (2018). Enhanced User Authentication with Privacy for IoT-Based Medical Care System. International Journal of Computer Theory and Engineering, 10, 125-129.
[4]
Dwork, C. (2006). Differential privacy. Automata, Languages and Programming, Pt 2, 4052, 1-12. <Go to ISI>://WOS:000239475900001
[5]
Sun, Z. K., Wang, Y. L., Shu, M. L., Liu, R. X., & Zhao, H. Q. (2019). Differential Privacy for Data and Model Publishing of Medical Data. Ieee Access, 7, 152103-152114. https://doi.org/10.1109/Access.2019.2947295
[6]
Zhang, S. Q., & Li, X. H. (2022). Differential privacy medical data publishing method based on attribute correlation. Scientific Reports, 12(1). https://doi.org/ARTN 15725
[7]
Fletcher, S., & Islam, M. Z. (2019). Decision Tree Classification with Differential Privacy: A Survey. Acm Computing Surveys, 52(4). https://doi.org/Artn 83
[8]
Wang, N., Xiao, X. K., Yang, Y., Zhao, J., Hui, S. C., Shin, H., Shin, J., & Yu, G. (2019). Collecting and Analyzing Multidimensional Data with Local Differential Privacy. 2019 Ieee 35th International Conference on Data Engineering (Icde 2019), 638-649. https://doi.org/10.1109/Icde.2019.00063
[9]
Zorarpaci, E., & Ozel, S. A. (2020). Differentially private 1R classification algorithm using artificial bee colony and differential evolution. Engineering Applications of Artificial Intelligence, 94. https://doi.org/ARTN 103813
[10]
Croft, W., Sack, J. R., & Shi, W. (2022). Differential Privacy via a Truncated and Normalized Laplace Mechanism. Journal of Computer Science and Technology, 37(2), 369-388. https://doi.org/10.1007/s11390-020-0193-z
[11]
Yang, J. G., Xiang, L. Y., Chen, R. D., Li, W. T., & Li, B. C. (2022). Differential Privacy for Tensor-Valued Queries. Ieee Transactions on Information Forensics and Security, 17, 152-164. https://doi.org/10.1109/Tifs.2021.3089884
[12]
EmilinShyni, C., & Swamynathan, S. (2011). Reason based Access Control for Privacy Protection in Object Relational Database Systems. International Journal of Computer Theory and Engineering, 32-37.
[13]
Gupta, M., & Joshi, R.C. (2009). Privacy Preserving Fuzzy Association Rules Hiding in Quantitative Data. International Journal of Computer Theory and Engineering, 382-388.
[14]
Patel, D.H., Kotadiya, H., & Vasant, A.R. (2022). Privacy-Preserving Association Rule Mining Considering Multi-objective through an Evolutionary Algorithm. International Journal of Computer Theory and Engineering.
[15]
Dwork, C. (2011). A Firm Foundation for Private Data Analysis. Communications of the Acm, 54(1), 86-95. https://doi.org/10.1145/1866739.1866758
[16]
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Theory of Cryptography, Proceedings, 3876, 265-284. https://doi.org/Doi 10.1007/11681878_14
[17]
Dwork, C. (2008). Differential privacy: A survey of results. Theory and Applications of Models of Computation, Proceedings, 4978, 1-19. https://doi.org/Doi 10.1007/978-3-540-79228-4_1
Index Terms
- LN-PCA: Differential Privacy Protection of Medical Data Based on Principal Component Analysis Adding Laplace Noise
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Published In
March 2023
824 pages
ISBN:9781450399029
DOI:10.1145/3594315
Copyright © 2023 ACM.
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Published: 02 August 2023
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ICCAI 2023
ICCAI 2023: 2023 9th International Conference on Computing and Artificial Intelligence
March 17 - 20, 2023
Tianjin, China
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View all- Peng SLi HWang WDong KYang MGuo T(2025)LDP-PPA: Local differential privacy protection for principal component analysisInformation Sciences10.1016/j.ins.2025.121962704(121962)Online publication date: Jun-2025
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