QuantumShield: A BB84-Driven Post-Quantum Cryptographic Framework for Secure Data Encryption with Hybrid Key Distribution
Keywords:
AES-256-GCM, BB84 Protocol, Lattice signatures, Post-quantum cryptography, Qiskit, Quantum key distributionAbstract
Contemporary public-key schemes such as RSA and Elliptic Curve Cryptography face an existential threat from quantum algorithms that solve their underlying hard problems in polynomial time. Although standards bodies have begun selecting post-quantum replacements, few working prototypes exist that bring these ideas into an accessible, interactive format. This paper describes QuantumShield, a browser-based encryption tool that unifies three complementary defence layers within a single pipeline. First, the BB84 quantum key distribution protocol is faithfully simulated through IBM’s Qiskit framework using a chunked-circuit approach, producing a 256-bit key after basis sifting, error-rate sampling, and SHA-256 privacy amplification. Second, the derived key drives AES-256-GCM authenticated encryption for payload protection. Third, a simplified Learning With Errors lattice signature provides post-quantum integrity verification over the ciphertext. Experiments across qubit counts from 64 to 1024 show a basis-match rate holding at the theoretical 50 %, zero quantum bit error rate in the noise-free simulator, and linear time growth averaging 90 ms per 20-qubit chunk. All 45 validation tests passed. QuantumShield runs entirely on commodity hardware and requires no cloud credentials, offering a cost-free reference platform for quantum-safe cryptography research and instruction.
References
P. W. Shor, “Algorithms for quantum computation: discrete logarithms and factoring,” Proceedings 35th Annual Symposium on Foundations of Computer Science, Santa Fe, NM, USA, 1994, pp. 124–134.
L. K. Grover, “A fast quantum mechanical algorithm for database search,” Proceedings of the twenty-eighth annual ACM symposium on Theory of Computing, Jul. 1996, pp. 212–219.
National Institute of Standards and Technology, “Post-quantum cryptography standardization,” 2024.
K. E, C. M. B. M J, R. S, A. D. S, S. Kannan and V. K, “Quantum-safe federated learning: Enhancing data privacy and security,” 2024 International Conference on Emerging Research in Computational Science (ICERCS), Coimbatore, India, 2024, pp. 1–6.
M. Xu et al., “Privacy-preserving intelligent resource allocation for federated edge learning in quantum internet,” in IEEE Journal of Selected Topics in Signal Processing, vol. 17, no. 1, pp. 142–157, Jan. 2023.
R. Zuo, H. Tian, Z. An, and F. Zhang, “Post-quantum privacy-preserving aggregation in federated learning based on lattice,” Cyberspace Safety and Security, Sep. 2022, pp. 314–326.
R. Rofougaran, S. Yoo, H. -H. Tseng and S. Y. -C. Chen, “Federated quantum machine learning with differential privacy,” ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp. 9811–9815.
Y. Qi, M. S. Hossain, J. Nie, and X. Li, “Privacy-preserving blockchain-based federated learning for traffic flow prediction,” Future Generation Computer Systems, vol. 117, pp. 328–337, Apr. 2021.
O. Regev, “On lattices, learning with errors, random linear codes, and cryptography,” Journal of the ACM, vol. 56, no. 6, pp. 1–40, Jan. 2024.
C. H. Bennett and G. Brassard, “Quantum cryptography: Public key distribution and coin tossing,” Theoretical Computer Science, vol. 560, no. 1, pp. 7–11, Dec. 2014.
M. J. Dworkin, Recommendation for Block Cipher Modes of Operation: Galois/Counter Mode (GCM) and GMAC, NIST Special Publication 800-38D, Nov. 2007.
