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Quantum Authentication for Data Security: A Review of Data
Leakage Protection Strategies
Abdullah Fairuzullah Ahmad Tajuddin, Noraziah Ahmad
Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah
*Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.914MG00243
Received: 04 December 2025; Accepted: 10 December 2025; Published: 24 December 2025
ABSTRACT
This research proposes and details a quantum authentication methodology as a promising and essential solution
for enhancing data security in credit card transactions, specifically focusing on data leakage protection. Unlike
conventional approaches such as blockchain, quantum authentication offers unbroken spoofing protection and
does not require extensive data archiving. The practical implementation involves the establishment of secure
communication channels using Quantum Key Distribution (QKD) between the Point-of-Sale (POS) terminal
and the bank, the dynamic generation of a quantum-enhanced CVV, and the encryption of transaction data
with post-quantum algorithms, including Lattice-based Cryptography, designed to resist future quantum
attacks. Data Leakage Protection (DLP) systems, incorporating tokenization and encryption, are integral to
secure data storage and processing throughout the entire transaction lifecycle. The methodology is underpinned
by rigorous mathematical proofs and integrates various quantum authentication methods, such as Key-
Controlled Maximally Mixed Quantum State Encryption, Three-Factor Quantum Biometric Authentication,
and a Triple Security Mechanism, to provide robust security against unauthorized access and fraudulent
activities. While offering significant advancements in security, challenges remain in its widespread adoption,
including complexities in implementation efficiency, potential for message collisions, seamless integration
with existing systems, and the requirement for specialized hardware. Furthermore, tokenization, though
crucial, faces limitations such as technical complexity, interoperability issues, ongoing maintenance, and
susceptibility to Electromagnetic Interference (EMI). This research highlights the critical need for continued
development to fully leverage quantum technologies in securing financial data.
Keywords: Data Security, Blockchain, Data Leakage Protection, QuantumAuthentication, Shor Algorithm,
Quantum Key Distribution (QKD), Post-Quantum Cryptography, Tokenization, Lattice-based Cryptography
INTRODUCTION
Data leakage protection (DLP) focuses on preventing unauthorized access by implementing quantum
authentication for data leakage protection, particularly for credit card transactions CCV, involves using the
principles of quantum mechanics to create highly secure encryption and authentication methods. This helps
prevent unauthorized access and fraudulent activities by leveraging the inherent randomness and unbreachable
nature of quantum states to verify user identities and protect sensitive financial data during transmission
(Lukas et al., 2023). However, many did not notice Language Models (LMs) are fundamental to many natural
language processing tasks as pre-trained LMs are adapted to downstream tasks by fine-tuning on domain-
specific datasets such as human dialogs which may contain private information about whom it contains
information, known as data extraction. Quantum authentication is emerging as a critical technology for
enhancing data security, particularly in protecting sensitive information such as credit card data.
Objective to be answer by any researcher. What are the current challenges in implementing quantum
authentication for credit card data leakage protection? How does the integration of quantum key distribution
impact the security of credit card transactions? And What are the potential benefits of using quantum
authentication for credit card data leakage protection in terms of enhanced security and reduced costs?
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Data Security in Credit Card
Practical Implementation of the quantum-secure credit card system could function as follows: When a
cardholder initiates a transaction, the POS terminal and bank use QKD to establish a secure session. The card
dynamically generates a quantum-enhanced CVV and transmits it securely to the bank. The transaction data,
encrypted with a post-quantum algorithm, is processed and verified. DLP systems monitor the entire process to
prevent unauthorized data access. Figure 1 indicate the process User/Cardholder: The individual using the
quantum-secure credit card for transactions, Quantum-Secure Credit Card: The physical card equipped with
dynamic CVV technology and quantum-generated cryptographic features, Merchant POS System: A point-of-
sale system capable of validating quantum-secure transactions, Online Payment Gateway: For online
purchases, this gateway uses QKD and post-quantum cryptography to secure the transaction data. Additional
Layel Bank/Issuer with QKD: The financial institution uses QKD for secure communication and implements
advanced encryption techniques. Finally, Data Leakage Protection: Ensures secure data storage and processing
with tokenization, encryption, and compliance frameworks.
Fig. 1 Overview QKD embeded to DLP Credit Card
Quantum Authentication
Quantum Authentication for Credit Card Security Quantum-Secure as Authentication leverages the unique
properties of quantum mechanics to create secure exchanges that are resistant to spoofing (Monyk, 2019). This
method is particularly effective in preventing unauthorized access to financial data (Rieffel & Polak, 2011),
such as credit card information represent in Figure 2, by utilizing quantum cryptography to enhance
security measures (Pavani & Daftary, 2017).
Fig. 2 Tamper Resistant
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Lattice-Base Cryptography
Major Protocol been current implement Quantum-Resistant Digital Signatures using Lattice-based
Cryptography is a method for implementing quantum-secure authentication to protect against data leakage,
particularly for credit card transactions. Lattice-based signatures leverage complex mathematical structures
called lattices to create digital signatures that are resistant to attacks from quantum computers, providing an
additional layer of security beyond traditional cryptographic methods. Basis vectors are the set of linearly
independent vectors that define the geometry of a lattice, with each vector representing one dimension of the
lattice structure.
Fig. 3 Lattice Basic Vector
Evolution Of Quantum Computing
Quantum computing is a new paradigm of computation that has the potential to revolutionize many fields,
including data loss prevention (DLP). However, there is no specific mathematical equation that directly relates
quantum computing to DLP for credit cards. Instead, quantum computing offers potential advantages in
enhancing the security and efficiency of various components involved in credit card DLP.
Biometric Authentication Incorporating biometric data (e.g., fingerprints or retinal scans) adds a layer of
security, requiring physical presence for authentication (Bhattacharyya, Ranjan, Alisherov, & Choi, 2009).This
dual requirement significantly reduces the risk of identity theft and unauthorized transactions. Advanced
Cryptographic Techniques The use of quantum permutation pad block ciphers and Clifford operators enhances
data confidentiality and integrity (Barbeau, 2023). Flexible quantum homomorphic encryption allows for
efficient identity authentication while minimizing resource consumption (X.-B. Chen, Sun, Xu, & Yang,
2019).
While quantum authentication presents a promising solution for data leakage protection, challenges remain in
implementation efficiency and the potential for message collisions in cryptographic schemes (Barbeau, 2023).
Balancing security with usability will be crucial for widespread adoption.
Optimized Data Analysis: Quantum algorithms could optimize the analysis of large volumes of data related to
credit card transactions, user behavior, and network activity. This can help identify anomalies and potential
data loss incidents more efficiently. Mathematical equations and quantum information theory are crucial in
developing these optimization algorithms.
Anomaly Detection and Statistical Methods: DLP systems often use statistical methods to establish a
baseline of normal data activity. Deviations from this baseline can indicate a potential data loss incident is
calculating the standard deviation
.
Quantum authentication plays a crucial role in protecting sensitive data, such as credit card information, from
leakage. By leveraging quantum mechanics, various innovative methods have been developed to enhance
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security and ensure the integrity of transactions. The following sections outline key approaches to executing
quantum authentication for data leakage protection.
Quantum Authentication
Key Quantum Authentication Methods can be dividing into 3 categories
First Category
Key-Controlled Maximally Mixed Quantum State Encryption: This method utilizes a single qubit and
unitary operations to improve efficiency in quantum authentication, particularly in noisy environments, making
it suitable for secure credit card transactions (Lim, Choi, Kang, Yang, & Han, 2023)
Second Category
Three-Factor Quantum Biometric Authentication: This approach combines quantum key distribution,
biometric identification, and machine learning to create a robust authentication system. It employs double-
layer encryption, significantly reducing unauthorized access risks (X. Chen, Wang, & Li, 2024)
Third Category
Triple Security Mechanism: This method integrates quantum unclonable, physical unclonable, and anti-
peeping mechanisms, ensuring that any unauthorized attempts to access sensitive data trigger alerts, thus
enhancing security for financial transactions (Li Mo et al., 2018)
Quantum Encryption-Based Payment Systems
Quantum Encryption Payment Systems
These systems utilize quantum keys for data encryption and authentication during payment processes, allowing
for secure transactions even for non-registered users. This method ensures the authenticity of both the payment
service and the message being transmitted (Arele & Sejwar, 2017)
In contrast, while quantum authentication offers significant advancements in security, challenges remain in its
widespread implementation, particularly regarding the integration with existing systems and the need for
specialized hardware. This highlights the ongoing need for research and development in practical applications
of quantum technologies. However, many Banking still have relied on Hash Algorithm, Blockchain and RSA
as main background of defence (G A, Prabha, Avudainayaki, Sundaram, & Nithyashri, 2024).
METHODOLOGY
The methodology for implementing quantum authentication for secure credit card transactions, as described in
the sources, involves mathematical proofs, a synthetic dataset structure, and a practical implementation
process. Mathematical Proofs: The methodology section includes mathematical proofs, such as the Gaussian
integral formula and vector norm inequalities. Proof
(1)
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(2)
Table 1 Synthetic dataset for Malaysian credit card usage in 2024 could look
Data
Card Number
(Tokenized)
Transaction
Amount
(MYR)
Merchant
Name
Location
Date
Age
Group
Gender
Card Number
(Tokenized)
1.
1234****5678
256.75
Lazada
Kuala
Lumpur
2024-
02-01
25-34
Male
1234****5678
2.
9876****5432
129.00
Amazon
Penang
2024-
02-02
18-24
Female
9876****5432
3.
4567****8910
310.50
Shopee
Johor
Bahru
2024-
02-03
35-44
Male
4567****8910
Tokenization
Tokenization: Replace card numbers with tokens to ensure no direct exposure. Tokenization is a technique
used to enhance credit card security by replacing sensitive payment data with a non-sensitive placeholder,
called a token (Das & Das, 2024). The main challenges and limitations of tokenization in credit card
processing include: 1) Complexity: Implementing and managing a tokenization system can be technically
complex, requiring specialized infrastructure and processes (Solat, 2017). 2) Interoperability: Ensuring
seamless integration and compatibility between different tokenization systems and payment platforms can be
challenging. 3) Ongoing Maintenance: Continuously updating and maintaining the tokenization system to keep
up with evolving security threats and industry standards requires ongoing effort and resources (Voldman,
2018). One of the challenges and limitations of tokenization is interference from Electromagnetic Interference
(EMI)(Tang, Hui, Chen, Guo, & Wu, 2023). EMI can disrupt the tokenization process and introduce errors, as
electromagnetic fields can induce currents in the electronic circuits handling the sensitive data, potentially
compromising the security of the tokenized information (M. Choi, Oh, Kim, Heo, & Kim, 2024; Myeongwon
Choi, Oh, Kim, & Kim, 2022).
Wireless Propagation Characteristics In many cases of wireless systems design and analysis, it is sufficient to
consider the power relationship between the transmitter and receiver to account for the propagation
characteristics of the environment. Specifically, we consider that the power. Using the formula it able to gain
relationship considerable amount propogation while using EMI intereference (Win, Pinto, & Shepp, 2009).
Aggregation: Group data by categories (e.g., location, age group) to prevent individual traceability
(3)
DISCUSSION
The research has shown that in compare the difference between Quantum Authentication, Hash Algorithm,
Lattice and QR Code from the current implementation security.
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Table 2 Comparison Between Algorithm
Author
Insisght
Contribution
Problems
Statement
Challenges
(Sunderajulu,
2024)
The paper does not specifically
address Data Leakage
Protection methods like
Quantum Authentication, Hash
Algorithm, CVV, or QR codes.
It focuses on payment fraud
types, risks, and mitigation
strategies rather than specific
data protection technologies.
Paper advocates for a
multi-faceted fraud
prevention strategy that
combines advanced
authentication protocols,
real-time monitoring,
and ongoing customer
awareness. This holistic
approach is vital for
maintaining the integrity
of payment ecosystems
and fostering consumer
trust
Payment
transaction
security is a
fundamental
concern.
Increasing
complexity of
payments
ecosystem
presents new
fraud challenges.
Complexity
of the
payments
ecosystem
presents new
challenges.
Emerging
technologies
require
scalable and
adaptable
security
solutions.
Author
Insisght
Contribution
Problems
Statement
Challenges
(Ahuja, Prabha,
& Garg, 2025)
Does not specifically address
Data Leakage Protection
methods like Quantum
Authentication, Hash
Algorithms, CVV, or QR
codes. It focuses on general E-
commerce security
technologies such as secure
payment gateways, encryption,
Multi-Factor Authentication,
and regulatory compliance.
Customer privacy is
crucial for E-commerce
growth. Advanced
technologies enhance
fraud protection and
security measures.
Data breaches
compromise
customer privacy
and data integrity.
Mistrust arises
among consumers
and retailers due
to security issues.
Data
breaches
compromise
customer
privacy and
data
integrity.
Mistrust
among
consumers
and retailers
due to
security risks
(Momeni,
Jabbari, &
Fung, 2024)
Offers significant insights into
the interplay between privacy,
security, regulatory
compliance, and efficiency in
mobile commerce payment
systems. These insights are
crucial for developing
effective and user-friendly
online payment solutions.
A token-based payment
system where the payer
uses a token valued and
signed by the bank to
make purchases. This
method not only
enhances security but
also simplifies the
payment process for
users
Many existing
online payment
systems do not
effectively
balance these
competing needs,
leading to
vulnerabilities
and user distrust.
Current solutions
may either
compromise user
privacy or fail to
provide adequate
security measures
against various
attacks
Performance
Efficiency
are not only
secure and
privacy-
preserving
but also
efficient and
lightweight.
Many
existing
systems are
either too
resource-
intensive or
slow, making
them
impractical
for everyday
use in mobile
commerce
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Fig. 4 Relationship between algorithm
CONCLUSION
The implementation of quantum authentication presents a promising and essential solution for enhancing data
security in credit card transactions, particularly as traditional cryptographic methods face increasing
vulnerability from advanced computing threats. The detailed methodology for this quantum-secure credit card
system integrates rigorous mathematical proofs, such as the Gaussian integral and vector norm inequalities, to
underpin its theoretical robustness. Practically, the system establishes secure sessions using Quantum Key
Distribution (QKD) between the Point-of-Sale (POS) terminal and the bank, dynamically generates a quantum-
enhanced CVV, and processes transaction data encrypted with post-quantum algorithms, including lattice-
based cryptography, to resist future quantum attacks. Data Leakage Protection (DLP) systems monitor the
entire process, utilizing tokenization to replace sensitive data with non-sensitive placeholders, thereby
preventing direct exposure of card numbers. However, tokenization, while effective, introduces challenges like
technical complexity, interoperability, and ongoing maintenance. Notably, Electromagnetic Interference (EMI)
can disrupt tokenization by inducing currents in electronic circuits, potentially compromising security. The
system's robustness is further bolstered by various quantum authentication methods, including Key-Controlled
Maximally Mixed Quantum State Encryption, Three-Factor Quantum Biometric Authentication, and a Triple
Security Mechanism.
Despite the significant advancements and robust security offered by quantum authentication, challenges remain
in its widespread adoption and implementation. These include the complexities of implementation efficiency,
the potential for message collisions in cryptographic schemes, and the need for seamless integration with
existing financial systems that predominantly rely on traditional methods like Hash Algorithms, Blockchain,
and RSA. Furthermore, the requirement for specialized hardware poses another practical hurdle. While
quantum authentication offers a powerful, future-proof approach to financial security by combining quantum
mechanics principles with established security practices, overcoming these practical and infrastructural
challenges through continued research and development will be crucial for its full integration and widespread
benefit in securing sensitive credit card information against evolving cyber threats
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ACKNOWLEDGEMENT
The author is thanking his teammates for their constructive comment and additional flavour of cherished
suggestion for the earlier draft paper, part of the work in this survey was carried out while the author was
working for Faculty Of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah
Conflict of Interest
Authors declare that there is no conflict of interests regarding the publication of the paper.
Author Contribution
The contributions of all authors must be described in the following manner:
The authors confirm contribution to the paper as follows: study conception and design: A.Fairuzullah,
A.Noraziah; data collection: A.Fairuzullah; analysis and interpretation of results: A.Fairuzullah,
A.Noraziah; draft manuscript preparation: A.Fairuzullah, A.Noraziah. All authors reviewed the results and
approved the final version of the manuscript.
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