International Journal of Engineering
Trends and Technology

Research Article | Open Access | Download PDF
Volume 74 | Issue 4 | Year 2026 | Article Id. IJETT-V74I4P106 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I4P106

A 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]