Privacy Aware Deep Learning Model for Multi Class Classification in Big Data
Jaya Sharma
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, Uttar Pradesh, India.
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, Uttar Pradesh, India.
Big data and deep learning (DL) are evolving technologies applied extensively in the medical field. Artificial intelligence (AI) technologies have simplified operations such as sharing and retrieving large medical images and swiftly providing disease results in no time. Sharing medical images that are highly sensitive information for every user might give away vulnerable information to the opponents. Privacy is a major concern between a user and a database. In this paper, we propose an Advanced Convolutional Neural Network (ACNN) for selecting the features from large-scale medical data, integrated with privacy-preserved cosine similarity (PPCS), to find similarities between users and all databank images securely. A comparison is made between an ACNN and a PPCS-ACNN based multi-class classification model for diagnosing various lung diseases from Computed Tomography (CT) images. The analysis focuses on the trade-offs between data privacy, diagnostic accuracy, and the efficiency of classification.
Keywords
Privacy Preserving, Image Classification, Big Data, Deep Learning.
U. Sivarajah, M. M. Kamal, Z. Irani, and V. Weerakkody, “Critical analysis of Big Data challenges and analytical methods,” Journal of Business Research, vol. 70, pp. 263–286, Jan. 2017, doi: 10.1016/j.jbusres.2016.08.001.
T. B. Murdoch and A. S. Detsky, “The Inevitable Application of Big Data to Health Care,” JAMA, vol. 309, no. 13, p. 1351, Apr. 2013, doi: 10.1001/jama.2013.393.
R. Ashraf et al., “Deep Convolution Neural Network for Big Data Medical Image Classification,” IEEE Access, vol. 8, pp. 105659–105670, 2020, doi: 10.1109/access.2020.2998808.
L. Hall, D. Goldgof, R. Paul, and G. M. Goldgof, “Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset,” Apr. 2020, doi: 10.36227/techrxiv. 12083964.v2.
J. Liu et al., “Applications of deep learning to MRI images: A survey,” Big Data Mining and Analytics, vol. 1, no. 1, pp. 1–18, Mar. 2018, doi: 10.26599/bdma.2018.9020001.
J. Tang, S. Alelyani, and H. Liu, “Feature selection for classification: A review,” Data Classif. Algorithms Appl., p. 37, 2014.
Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res., vol. 3, no. Mar, pp. 1157–1182, 2003.
S. Ono, J. Takata, M. Kataoka, T. I, K. Shin, and H. Sakamoto, “Privacy-Preserving Feature Selection with Fully Homomorphic Encryption,” Algorithms, vol. 15, no. 7, p. 229, Jun. 2022, doi: 10.3390/a15070229.
S. Saha et al., “Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm,” Applied Sciences, vol. 10, no. 8, p. 2816, Apr. 2020, doi: 10.3390/app10082816.
M. Ravi Prasad and N. Thillaiarasu, “Multichannel EfficientNet B7 with attention mechanism using multimodal biometric- based authentication for ATM transaction,” Multiagent and Grid Systems, vol. 20, no. 2, pp. 89–108, Aug. 2024, doi: 10.3233/mgs-230118.
H. Phung and E. J. Rhee, “A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets,” Applied Sciences, vol. 9, no. 21, p. 4500, Oct. 2019, doi: 10.3390/app9214500.
S. Hosseinzadeh Kassani and P. Hosseinzadeh Kassani, “A comparative study of deep learning architectures on melanoma detection,” Tissue and Cell, vol. 58, pp. 76–83, Jun. 2019, doi: 10.1016/j.tice.2019.04.009.
K. Huang, X. Liu, S. Fu, D. Guo, and M. Xu, “A Lightweight Privacy-Preserving CNN Feature Extraction Framework for Mobile Sensing,” IEEE Transactions on Dependable and Secure Computing, pp. 1–1, 2020, doi: 10.1109/tdsc.2019.2913362.
D. Franklin Vinod and V. Vasudevan, “PPCS-MMDML: Integrated Privacy-Based Approach for Big Data Heterogeneous Image Set Classification,” in Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2018, Volume 1, Springer, 2019, pp. 435–443.
C. Guo, J. Jia, K.-K. R. Choo, and Y. Jie, “Privacy-preserving image search (PPIS): Secure classification and searching using convolutional neural network over large-scale encrypted medical images,” Computers & Security, vol. 99, p. 102021, Dec. 2020, doi: 10.1016/j.cose.2020.102021.
S. M and N. Thillaiarasu, “A Survey on Different Computer Vision Based Human Activity Recognition for Surveillance Applications,” 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1372–1376, Mar. 2022, doi: 10.1109/iccmc53470.2022.9753931.
E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification,” MedRxiv, p. 2020.04. 24.20078584, 2020.
S. Armato et al., “WE-B-201B-02: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Public Database of CT Scans for Lung Nodule Analysis,” Medical Physics, vol. 37, no. 6Part6, pp. 3416–3417, Jun. 2010, doi: 10.1118/1.3469350.
W. Ning et al., “iCTCF: an integrative resource of chest computed tomography images and clinical features of patients with COVID-19 pneumonia,” Apr. 2020, doi: 10.21203/rs.3.rs-21834/v1.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Jaya Sharma and Franklin Vinod D;
Methodology: Franklin Vinod D;
Software: Jaya Sharma;
Data Curation: Jaya Sharma and Franklin Vinod D;
Writing- Original Draft Preparation: Jaya Sharma and Franklin Vinod D;
Visualization: Jaya Sharma;
Investigation: Jaya Sharma and Franklin Vinod D;
Supervision: Franklin Vinod D;
Validation: Jaya Sharma;
Writing- Reviewing and Editing: Jaya Sharma and Franklin Vinod D;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr. Franklin Vinod D for this research completion and support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
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, Ghaziabad, Uttar Pradesh, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Jaya Sharma and Franklin Vinod D, “Privacy Aware Deep Learning Model for Multi Class Classification in Big Data”, Journal of Machine and Computing, pp. 924-932, April 2025, doi: 10.53759/7669/jmc202505073.
