International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-122 January-2025
  1. 4774
    Citations
  2. 2.8
    CiteScore
Enhancing cloud data security with biometrics-based encryption and machine learning

Safa Ismael Ibrahim1, Dalal Abdulmohsin Hammood1 and Leith Hamid Abed2

Electrical Engineering College,Middle Technical University, Baghdad 10022,Iraq1
Department of Computer System,Technical Institute of Anbar, Medical Technical University, Baghdad,Iraq2
Corresponding Author : Safa Ismael Ibrahim

Recieved : 24-April-2024; Revised : 18-January-2025; Accepted : 22-January-2025

Abstract

This paper addresses the growing security concerns in cloud computing by proposing a robust and unobtrusive data protection mechanism. The study leverages secure, unobtrusive biometrics to enhance the security of data stored in the cloud. Cloud computing’s scalability and cost-effectiveness have driven its widespread adoption, yet securing sensitive data remains a challenge as cyberattacks on cloud infrastructures increase. To tackle this issue, a comprehensive framework is presented that integrates biometric authentication and encryption algorithms to protect data at rest in the cloud. The approach employs biometric traits, such as fingerprints, iris patterns, or facial features, to generate encryption keys, providing an additional security layer without burdening users. The methodology includes data preprocessing, feature extraction, biometric cancellation and authentication, and file upload and protection. Initially, facial features from unconstrained images are normalized and resized during preprocessing. A convolutional neural network (CNN) then extracts discriminative features. These features undergo biometric cancellation using the Lorenz chaotic algorithm and compressive sensing generalized likelihood ratio test (CS-GLRT) algorithm, ensuring enhanced security during recognition. The advanced encryption standard (AES) further secures the data. After successful authentication, files are uploaded and protected using an application programming interface (API) secured by AES. The proposed framework was evaluated using machine learning models, including support vector machines (SVM), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost). The dataset was split into 80% for training and 20% for testing, with each model trained for 100 epochs. The results indicate that the SVM classifier achieved the highest accuracy at 99.8%, making it the most suitable for unconstrained face image classification. This work demonstrates a novel and effective approach to safeguarding cloud-stored data, addressing critical security concerns while maintaining user convenience.

Keywords

Cloud computing security, Biometric authentication, Data encryption, Machine learning, Advanced encryption standard, Convolutional neural networks.

Cite this article

Ibrahim SI, Hammood DA, Abed LH. Enhancing cloud data security with biometrics-based encryption and machine learning. International Journal of Advanced Technology and Engineering Exploration. 2025;12(122):132-146. DOI : 10.19101/IJATEE.2024.111100624

References
[1]
Gupta S, Maple C, Crispo B, Raja K, Yautsiukhin A, Martinelli F. A survey of human-computer interaction (HCI) & natural habits-based behavioural biometric modalities for user recognition schemes. Pattern Recognition. 2023; 139:109453.
[Crossref] [Google Scholar]
[2]
Li Y. Research and application of deep learning in image recognition. In 2nd international conference on power, electronics and computer applications 2022 (pp. 994-9). IEEE.
[Crossref] [Google Scholar]
[3]
Wang S, Deng G, Hu J. A partial hadamard transform approach to the design of cancelable fingerprint templates containing binary biometric representations. Pattern Recognition. 2017; 61:447-58.
[Crossref] [Google Scholar]
[4]
Yang W, Wang S, Hu J, Zheng G, Valli C. A fingerprint and finger-vein based cancelable multi-biometric system. Pattern Recognition. 2018; 78:242-51.
[Crossref] [Google Scholar]
[5]
Shahzad M, Wang S, Deng G, Yang W. Alignment-free cancelable fingerprint templates with dual protection. Pattern Recognition. 2021; 111:107735.
[Crossref] [Google Scholar]
[6]
Bedari A, Wang S, Yang W. Design of cancelable MCC-based fingerprint templates using dyno-key model. Pattern Recognition. 2021; 119:108074.
[Crossref] [Google Scholar]
[7]
Maiorana E. A survey on biometric recognition using wearable devices. Pattern Recognition Letters. 2022; 156:29-37.
[Crossref] [Google Scholar]
[8]
Hammadi OI, Abas AD, Ayed KH. Face recognition using deep learning methods a review. International Journal of Engineering & Technology. 2018; 7:6181-8.
[Crossref] [Google Scholar]
[9]
Melzi P, Tolosana R, Vera-rodriguez R, Kim M, Rathgeb C, Liu X, et al. FRCSyn-ongoing: nenchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems. Information Fusion. 2024; 107:1-19.
[Crossref] [Google Scholar]
[10]
Zulfiqar M, Syed F, Khan MJ, Khurshid K. Deep face recognition for biometric authentication. In international conference on electrical, communication, and computer engineering 2019 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[11]
Semwal A, Londhe ND. A multi-stream spatio-temporal network based behavioural multiparametric pain assessment system. Biomedical Signal Processing and Control. 2024; 90:105820.
[Crossref] [Google Scholar]
[12]
Singhal N, Ganganwar V, Yadav M, Chauhan A, Jakhar M, Sharma K. Comparative study of machine learning and deep learning algorithm for face recognition. Jordanian Journal of Computers and Information Technology. 2021; 7(3):313-25.
[Google Scholar]
[13]
Sawat DD, Hegadi RS. Unconstrained face detection: a deep learning and machine learning combined approach. CSI Transactions on ICT. 2017; 5:195-9.
[Crossref] [Google Scholar]
[14]
Moghekar R, Ahuja S. Face recognition in unconstrained environment using deep learning. In soft computing for intelligent systems: proceedings of ICSCIS 2020 (pp. 241-53). Springer Singapore.
[Crossref] [Google Scholar]
[15]
Adetunji TO. Machine learning algorithms in facial identity verification for computer-based assessments. Acta Electronica Malaysia. 2024; 8(2):54-9.
[Crossref] [Google Scholar]
[16]
Qasim KR, Qasim SS. Force field feature extraction using FAST algorithm for face recognition performance. In journal of physics: conference series 2021 (p. 012195). IOP Publishing.
[Crossref] [Google Scholar]
[17]
Vakhshiteh F, Nickabadi A, Ramachandra R. Adversarial attacks against face recognition: a comprehensive study. IEEE Access. 2021; 9:92735-56.
[Crossref] [Google Scholar]
[18]
Sharma R. Biometric authentication using lightweight convolutional neural network. In international students' conference on electrical, electronics and computer science 2024 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[19]
Kauba C, Piciucco E, Maiorana E, Gomez-barrero M, Prommegger B, Campisi P, et al. Towards practical cancelable biometrics for finger vein recognition. Information Sciences. 2022; 585:395-417.
[Crossref] [Google Scholar]
[20]
Liu Y, Zhang X, Li Y, Zhou J, Li X, Zhao G. Graph-based facial affect analysis: a review. IEEE Transactions on Affective Computing. 2022; 14(4):2657-77.
[Crossref] [Google Scholar]
[21]
Ma Q, Li B, Liu G, Li Y, Wang Y, Gu M, et al. Cancelable face template protection based on deep neural network. In 7th international conference on signal and image processing 2022 (pp. 659-64). IEEE.
[Crossref] [Google Scholar]
[22]
Helmy M, El-shafai W, El-rabaie ES, El-dokany IM, Abd EFE. A hybrid encryption framework based on Rubik’s cube for cancelable biometric cyber security applications. Optik. 2022; 258:168773.
[Crossref] [Google Scholar]
[23]
Acar A. Privacy-aware security applications in the era of internet of things. Electronic Theses and Dissertations, Florida International University. 2020.
[Crossref] [Google Scholar]
[24]
Sudhakar T, Gavrilova M. Deep learning for multi-instance biometric privacy. ACM Transactions on Management Information Systems. 2020; 12(1):1-23.
[Crossref] [Google Scholar]
[25]
Abdellatef E, Ismail NA, Abd Elrahman SE, Ismail KN, Rihan M, Abd El-Samie FE. Cancelable fusion-based face recognition. Multimedia Tools and Applications. 2019; 78:31557-80.
[Crossref] [Google Scholar]
[26]
Almomani I, El-Shafai W, AlKhayer A, Alsumayt A, Aljameel S, Alissa K. Proposed biometric security system based on deep learning and chaos algorithms. Computers, Materials & Continua. 2023; 74(2):3515-37.
[Crossref] [Google Scholar]
[27]
Hassaballah M, Aly S. Face recognition: challenges, achievements and future directions. IET Computer Vision. 2015; 9(4):614-26.
[Crossref] [Google Scholar]
[28]
Abdellatef E, Ismail NA, Abd Elrahman SE, Ismail KN, Rihan M, Abd El-Samie FE. Cancelable multi-biometric recognition system based on deep learning. The Visual Computer. 2020; 36:1097-109.
[Google Scholar]