A Privacy Based Deep Learning Algorithm for Big Data Analytics

Abstract

This thesis addresses critical challenges in privacy-preserving feature selection and classification for big data analytics. Specifically, four novel methodologies are proposed: Hierarchical Classification Feature Selection (HCFS), Privacy-Preserving Classification Selection with p-stability (PPCS), Local N-ternary Pattern combined with Modified Deep Belief Network (LNTP-MDBN), and Privacy-Preserving Cosine Similarity integrated with Multi-Manifold Deep Metric Learning (PPCS-MMDML). These approaches collectively enhance classification accuracy, optimize feature extraction from heterogeneous image sets, and robustly preserve privacy, demonstrating significant improvements in data-driven analytical applications.

Author Biographies

Franklin Vinod D, Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, NCR Campus, Delhi-NCR Campus, Delhi-Meerut Road, Modinagar, Ghaziabad, UP, India

Assistant Professor

Neha Ahlawat, Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, NCR Campus, Delhi-NCR Campus, Delhi-Meerut Road, Modinagar, Ghaziabad, UP, India

Assistant Professor

References

W. Dou, X. Zhang, J. Liu and J. Chen, Hiresome-II: Towards privacy aware cross-cloud service composition for big data applications, IEEE Trans Parallel Distrib Syst., 6(2), (2014), 455–466.

A.T. Azar and A.E. Hassanien, Dimensionality reduction of medical big data using neural-fuzzy classifier, Soft Computing, 19, (2015), 1115-1127.

S. Gao, Z. Zeng, K. Jia, T.-H. Chan and J. Tang, Patch-Set-Based Representation for Alignment-Free Image Set Classification, IEEE Trans.Cir. and Sys. for Video Tech., 26, (2016), 1646-1658.

D. F. Vinod and V. Vasudevan, "A filter-based feature set selection approach for big data classification of patient records," 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, 2016, pp. 3684-3687, doi: 10.1109/ICEEOT.2016.7755397.

Franklin Vinod, D., Vasudevan, V. (2017). A Bi-level Security Mechanism for Efficient Protection on Graphs in Online Social Network. In: Arumugam, S., Bagga, J., Beineke, L., Panda, B. (eds) Theoretical Computer Science and Discrete Mathematics. ICTCSDM 2016. Lecture Notes in Computer Science(), vol 10398. Springer, Cham.

Vinod DF, Vasudevan V. LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification. Curr Med Imaging Rev. 2019;15(2):227-236. doi: 10.2174/1573405613666170721103949. PMID: 31975670

Franklin Vinod, D., Vasudevan, V. (2019). PPCS-MMDML: Integrated Privacy-Based Approach for Big Data Heterogeneous Image Set Classification. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 106. Springer, Singapore

Authors

  • Franklin Vinod D Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, NCR Campus, Delhi-NCR Campus, Delhi-Meerut Road, Modinagar, Ghaziabad, UP, India
  • Neha Ahlawat Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, NCR Campus, Delhi-NCR Campus, Delhi-Meerut Road, Modinagar, Ghaziabad, UP, India

DOI:

https://doi.org/10.31449/inf.v49i2.8763

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Published

05/06/2025

How to Cite

D, F. V., & Ahlawat, N. (2025). A Privacy Based Deep Learning Algorithm for Big Data Analytics. Informatica, 49(2). https://doi.org/10.31449/inf.v49i2.8763

Issue

Vol. 49 No. 2 (2025)

Section

Thesis summary

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