Research Article | Open Access | Download PDF
Volume 74 | Issue 4 | Year 2026 | Article Id. IJETT-V74I4P106 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I4P106A Cryptographic Approach to Privacy-Preserving Credit Card Security Using PFYHD-CVV and KYM-ECC
Venkatesh Kumar M, C. Lakshmi
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 04 Aug 2025 | 05 Feb 2026 | 12 Feb 2026 | 29 Apr 2026 |
Citation :
Venkatesh Kumar M, C. Lakshmi, "A Cryptographic Approach to Privacy-Preserving Credit Card Security Using PFYHD-CVV and KYM-ECC," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 4, pp. 69-86, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I4P106
Abstract
In today’s digital world, secure Credit Card Transactions (CCT) are crucial, especially over a wireless network. None of the existing works overcame the web skimming and shoulder surfing attacks during CCT. Hence, this paper proposes a novel Permutation Fisher-Yates Hexadecimal Card Verification Value (PFYHD-CVV)-based CCV generation to avoid web skimming and shoulder surfing attacks during CCT. Primarily, the customer registers on the E-commerce application. Here, by using the PFYHD-CVV technique, the 3-digit Card Verification Value (CVV) is dynamically generated. Next, the public and private keys are generated using the Kaplan-Yorke Map-Elliptic Curve Cryptographic (KYM-ECC) method. Then, utilizing the eXclusive OR Merchant Product-identification-Menezes-Qu-Vanstone (XORMP-MQV) method, the secret key is created. Then, the merchant-product legitimacy is authenticated using the Entropy Inverse Message Digest 5 Digital Signature Algorithm (EIMD5-DSA. Also, the payment processing webpage’s Internet Protocol (IP) address is spoofed via Right Shift 2’s Complement (RS2C). Lastly, credit card details are encrypted using the KYM-ECC. Therefore, the proposed work performs secured CCT with a security level of 98.8564%, showing better performance than the prevailing works.
Keywords
Secure Wireless Payment Protocol, IP Spoofing, Data Privacy Preservation, Digital Signature Algorithm, Authentication.
References
[1] Saqib Saeed, “A Customer-Centric view of
E-Commerce Security and Privacy,” Applied Sciences, vol. 13, no. 2, pp.
1-22, 2023.
[CrossRef] [Google Scholar] [Publisher
Link]
[2] Qianqian Li et al., “A Symmetric
Projection Space and Adversarial Training Framework for Privacy-Preserving
Machine Learning with Improved Computational Efficiency,” Applied Sciences,
vol. 15, no. 6, pp. 1-26, 2025.
[CrossRef]
[Google Scholar] [Publisher
Link]
[3] Mengna Yang, Yejun He, and Jian Qiao,
“Federated Learning-based Privacy-Preserving and Security: Survey,” 2021
Computing, Communications and IoT Applications, Shenzhen, China, pp. 312-317,
2021.
[CrossRef] [Google Scholar] [Publisher
Link]
[4] Mohammed Naif Alatawi, “Detection of
Fraud in IoT based Credit Card Collected Dataset using Machine Learning,” Machine
Learning with Applications, vol. 19, pp. 1-16, 2025.
[CrossRef]
[Google Scholar] [Publisher Link]
[5] A.F.M. Shahen Shah et al., “On the Vital
Aspects and Characteristics of Cryptocurrency-A Survey,” IEEE Access,
vol. 11, pp. 9451-9468, 2023.
[CrossRef]
[Google Scholar] [Publisher
Link]
[6] Vishnu Laxman et al., “Emerging Threats
in Digital Payment and Financial Crime: A Bibliometric Review,” Journal of
Digital Economy, vol. 3, pp. 205-222, 2024.
[CrossRef] [Google Scholar] [Publisher
Link]
[7] Ibrahim Y. Hafez et al., “A Systematic
Review of AI-Enhanced Techniques in Credit Card Fraud Detection,” Journal of
Big Data, vol. 12, no. 1, pp. 1-35, 2025.
[CrossRef]
[Google Scholar] [Publisher Link]
[8] S. Siva Alagesh et al., “Privacy
Preservation using Block chain for Credit Card Data,” 2022 2nd
International Conference on Innovative Practices in Technology and Management
(ICIPTM), Gautam Buddha Nagar, India, pp. 725-730, 2022.
[CrossRef]
[Google Scholar] [Publisher
Link]
[9] Latifa Albshaier, Seetah Almarri, and
M.M. Hafizur Rahman, “A Review of Blockchain’s Role in E-Commerce Transactions:
Open Challenges, and Future Research Directions,” Computers, vol. 13,
no. 1, pp. 1-42, 2024.
[CrossRef]
[Google Scholar] [Publisher
Link]
[10] Qiwei Han et al., “Towards
Privacy-Preserving Digital Marketing: An Integrated Framework for User Modeling
using Deep Learning on a Data Monetization Platform,” Electronic Commerce
Research, vol. 23, no. 3, pp. 1701-1730, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] S. Surya et al., “Protecting Online
Transactions: A Cybersecurity Solution Model,” 2023 3rd
International Conference on Advance Computing and Innovative Technologies in
Engineering (ICACITE),
Greater Noida, India, pp. 2630-2634, 2023.
[CrossRef] [Google Scholar] [Publisher
Link]
[12] Rejwan Bin Sulaiman, Vitaly Schetinin,
and Paul Sant, “Review of Machine Learning Approach on Credit Card Fraud
Detection,” Human-Centric Intelligent Systems, vol. 2, no. 1-2, pp.
55-68, 2022.
[CrossRef]
[Google Scholar] [Publisher Link]
[13] Xiaopeng LUO et al., “The Utility Impact
of Differential Privacy on Credit Card Data in Machine Learning Algorithms,” Procedia
Computer Science, vol. 221, pp. 664-672, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Oluwabusayo Adijat Bello et al.,
“Machine Learning Approaches for Enhancing Fraud Prevention in Financial
Transactions,” International Journal of Management
Technology, vol. 10, no. 1, pp, 85-108, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Zlatan Morić et al., “Protection of
Personal Data in the Context of E-Commerce,” Journal of Cybersecurity and
Privacy, vol. 4, no. 3, pp. 731-761, 2024.
[CrossRef] [Google Scholar] [Publisher
Link]
[16] Sena Efsun Cebeci, Kubra Nari, and Enver
Ozdemir, “Secure E-Commerce Scheme,” IEEE Access, vol. 10, pp.
10359-10370, 2022.
[CrossRef]
[Google Scholar] [Publisher
Link]
[17] S. Gandhimathi, and J. Soundarya,
“Credit Card Transaction Security using Facial Recognition Technology,” International
Journal of Scientific Research in Computer Science Engineering and Information
Technology, vol. 11, no. 2, pp. 2358-2365, 2025.
[CrossRef] [Publisher Link]
[18] Shee-Ihn Kim, and Seung-Hee Kim,
“E-Commerce Payment Model using Blockchain,” Journal of Ambient Intelligence
and Humanized Computing, vol. 13, no. 3, pp. 1673-1685, 2020.
[CrossRef]
[Google Scholar] [Publisher Link]
[19] Abdul Razaque et al., “Credit
Card-Not-Present Fraud Detection and Prevention using Big Data Analytics
Algorithms,” Applied Sciences, vol. 13, no. 1, pp. 1-27, 2022.
[CrossRef]
[Google Scholar] [Publisher
Link]
[20] Tahani Baabdullah et al., “Efficiency of
Federated Learning and Blockchain in Preserving Privacy and Enhancing the
Performance of Credit card Fraud Detection (CCFD) Systems,” Future Internet,
vol. 16, no. 6, pp. 1-22, 2024.
[CrossRef]
[Google Scholar] [Publisher
Link]
[21] Ming-Hour Yang et al., “Contactless
Credit Cards Payment Fraud Protection by Ambient Authentication,” Sensors,
vol. 22, no. 5, pp. 1-22 , 2022.
[CrossRef]
[Google Scholar] [Publisher
Link]
[22] Habib Ullah Khan et al., “Role of
Authentication Factors in Fin-Tech Mobile Transaction Security,” Journal of
Big Data, vol. 10, no. 1, pp. 1-37, 2023.
[CrossRef]
[Google Scholar] [Publisher Link]
[23] Emmanuel Ileberi, Yanxia Sun, and
Zenghui Wang, “A Machine Learning based Credit Card Fraud Detection using the
GA Algorithm for Feature Selection,” Journal of Big Data, vol. 9, no. 1,
pp. 1-17, 2022.
[CrossRef]
[Google Scholar] [Publisher Link]
[24] Mishall Al-Zubaidie, and Ghanima Sabr
Shyaa, “Applying Detection Leakage on Hybrid Cryptography to Secure Transaction
Information in E-Commerce Apps,” Future Internet, vol. 15, no. 8, pp.
1-20, 2023.
[CrossRef]
[Google Scholar] [Publisher
Link]
[25] Louisa Uchikoshi, and Björn Frank,
“Restoring Consumer Trust in E-Commerce: The Role of Blockchain Technology and
its Behavioral Implications,” Technological Forecasting and Social Change,
vol. 224, pp. 1-14, 2025.
[CrossRef]
[Google Scholar] [Publisher Link]
[26] Alexandros I. Bermperis et al.,
“On-Device Privacy-Preserving Fraud Detection for Smart Consumer Environments
using Federated Learning,” Applied Sciences, vol. 16, no. 2, pp. 1-16,
2026.
[CrossRef]
[Google Scholar] [Publisher
Link]
[27] Wang Junhai, Wang Yunfeng, and Othman
Ibrahim, “Explainable E-Commerce Transaction Prediction using LightGBM and
Permutation Importance,” IEEE Access, vol. 14, pp. 10153-10169, 2026.
[CrossRef]
[Google Scholar] [Publisher
Link]
[28] Kartik Shenoy, Credit Card Transactions
Fraud Detection Dataset, Kaggle,
2020. [Online]. Available: https://www.kaggle.com/datasets/kartik2112/fraud-detection
[29] Bhadra Mohit, Credit Card Fraud
Detection Trends and Tactics in Modern Credit Card Fraud, Kaggle, 2024. [Online]. Available: https://www.kaggle.com/datasets/bhadramohit/credit-card-fraud-detection
[30] Yunyeong Goh et al., “Secure Trust-based
Delegated Consensus for Blockchain Frameworks using Deep Reinforcement
Learning,” IEEE Access, vol. 10, pp. 118498-118511, 2022.
[CrossRef]
[Google Scholar] [Publisher
Link]
[31] Pavan Kumar Joshi, and Rajesh Kotha, “An
In-depth Knowledge on EMV Tags and their Adoption in FinTech and Traditional
Banking,” Journal of Scientific and
Engineering Research, vol. 10, no. 9, pp. 77-86, 2023.
[CrossRef]
[Google Scholar] [Publisher
Link]
[32] Jamal Habibi Markani et al., “Security
Establishment in ADS-B by Format-Preserving Encryption and Blockchain Schemes,”
Applied Sciences, vol. 13, no. 5, pp. 1-16, 2023.
[CrossRef]
[Google Scholar] [Publisher
Link]
[33] Larisa Găbudeanu et al., “Privacy
Intrusiveness in Financial-Banking Fraud Detection,” Risks, vol. 9, no.
6, pp. 1-22, 2021.
[CrossRef]
[Google Scholar] [Publisher
Link]
[34] Yoonyoung Hwang, Sangwook Park, and Nina
Shin, “Sustainable Development of a Mobile Payment Security Environment using
Fintech Solutions,” Sustainability, vol. 13, no. 15, pp. 1-15, 2021.
[CrossRef]
[Google Scholar] [Publisher
Link]