Differential privacy based classification model for mining medical data stream using adaptive random forest
By: Hayder K. Fatlawi and Attila Kiss
References
- [1] A. Al-Fatlawi, H. Fatlawi, S. H. Ling Recognition physical activities with optimal number of wearable sensors using data mining algorithms and deep belief network, 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC) Seogwipo, South Korea, 2017, pp. 2871–2874. ⇒7
- [2] B. Babenko, MH. Yang, S. Belongie, A family of online boosting algorithms, 2009 IEEE 12th international conference on computer vision workshops, ICCV workshops Kyoto, Japan, 2009, pp. 1346–1353. ⇒6, 7, 8
- [3] P. R. Bhaladhare, D. C. Jinwala, Novel Approaches for Privacy Preserving Data Mining in k-Anonymity Model, Journal of information science and engineering, 32 (2016) 63–78. ⇒3, 5
- [4] A. Bifet, G. Holmes, B. Pfahringer, G. Bernhard, Improving adaptive bagging methods for evolving data streams, Asian conference on machine learning Nanjing, China, 2009, pp. 23–37. ⇒11
- [5] K. Chaudhuri, C. Monteleoni, Privacy-preserving logistic regression, Advances in neural information processing systems Vancouver, Canada, 2009, pp. 289–296. ⇒3, 5, 8
- [6] P. Domingos, G. Hulten, Mining high-speed data streams, KDD00: the second annual international conference on knowledge discovery in data Boston Massachusetts, USA, 2000, pp. 71–80. ⇒10
- [7] C. Dwork, G. N. Rothblum, S. Vadhan, Boosting and differential privacy, 2010 IEEE 51st annual symposium on foundations of computer science Las Vegas, Nevada USA, 2010, pp. 51–60. ⇒3, 5, 8
- [8] H. Fatlawi, A. Kiss, On Robustness of Adaptive Random Forest Classifier on Biomedical Data Stream, Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020), Lecture notes in computer science Springer, 12033 (2020) 332-344. ⇒4
- [9] J. Gama, Knowledge discovery from data streams, The CRC Press, 2010. ⇒6, 7
- [10] G. Giovanni, J. F. Elder, Ensemble methods in data mining: improving accuracy through combining predictions, The Synthesis lectures on data mining and knowledge discovery Morgan & Claypool Publishers, 2010. ⇒7
- [11] G. Hulten, L. Spencer, P. Domingos, Mining time-changing data streams, Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining San Francisco California, USA, 2001, pp. 97–106. ⇒2
- [12] M. A. Kadampur, D.V.L.N Somayajulu, A noise addition scheme in decision tree for privacy preserving data mining, Journal of computing 2, 1 (2010) 137–144. ⇒3, 5
- [13] M. Kuhn, K. Johnson, Applied predictive modeling, Springer, 2013. ⇒7
- [14] L. Li, R. Lu, K. R. Choo, A. Datta,J. Shao, Privacy-preserving-outsourced association rule mining on vertically partitioned databases, IEEE transactions on information forensics and security, 11, 8 (2016) 1847–1861. ⇒3, 5
- [15] V. Manikandan, V. Porkodi, A. S. Mohammed, M. Sivaram, Privacy preserving data Mining using threshold based fuzzy C-Means clustering, ICTACT journal on soft computing, 9, 1 (2018) 1820–1823. ⇒4, 5
- [16] J. J. v. Nayahi, V. Kavitha, Privacy and utility preserving data clustering for data anonymization and distribution on Hadoop, Future Generation Computer Systems, 74 (2017) 393–408. ⇒4, 5
- [17] T. Ngo, Data mining: practical machine learning tools and technique, by ian h. witten, eibe frank, mark a. hell, The ACM SIGSOFT Software Engineering Notes, 36, 5 2011. ⇒7
- [18] N.C. Oza, Online bagging and boosting, 2005 IEEE international conference on systems, man and cybernetics Waikoloa, HI, USA, 2005, pp. 2340–2345. ⇒10
- [19] D. L. Quoc, M. Beck, P. Bhatotia, R. Chen, Christof Fetzer, Thorsten Strufe, PrivApprox: privacy-preserving stream analytics, 2017 annual technical conference (USENIX ATC ’17) Santa Clara, CA, USA 2017, pp. 659–672. ⇒4, 5
- [20] J. Vaidya, B. Shafiq, W. Fan, D. Mehmood, D. Lorenzi, A random decision tree framework for privacy-preserving data mining, IEEE transactions on dependable and secure computing, 11, 5 (2013) 399–411. ⇒3, 5
- [21] Y. Wang, X. Wu, D. Hu, Using Randomized Response for Differential Privacy Preserving Data Collection, EDBT/ICDT Workshops, Bordeaux, France, 1558 (2016). ⇒3, 5
- [22] L. Zhang, Y. Liu, R. Wang, X. Fu, Q. Lin, E cient privacy-preserving classification construction model with differential privacy technology, Journal of systems engineering and electronics BIAI, 28, 1 (2017) 170–178. ⇒4, 5
- [23] Z. Zhou, Ensemble methods: foundations and algorithms, The CRC press, 2012. ⇒7
- [24] T. Zhu, G. Li, W. Zhou, S. Y. Philip, Differential privacy and applications, Springer, 2017. ⇒8, 10
DOI: https://doi.org/10.2478/ausi-2021-0001 | Journal eISSN: 2066-7760
Language: English
Page range: 1 - 20
Submitted on: Jan 23, 2021
Accepted on: Feb 28, 2021
Published online: Jul 8, 2021
Published in print: Jun 1, 2021
Published by: Sapientia Hungarian University of Transylvania
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
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© 2021 Hayder K. Fatlawi, Attila Kiss, published by Sapientia Hungarian University of Transylvania
This work is licensed under the Creative Commons Attribution 4.0 License.