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

Downloads

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

License

Authors retain copyright in their work. By submitting to and publishing with Informatica, authors grant the publisher (Slovene Society Informatika) the non-exclusive right to publish, reproduce, and distribute the article and to identify itself as the original publisher.

All articles are published under the Creative Commons Attribution license CC BY 3.0. Under this license, others may share and adapt the work for any purpose, provided appropriate credit is given and changes (if any) are indicated.

Authors may deposit and share the submitted version, accepted manuscript, and published version, provided the original publication in Informatica is properly cited.