Lightweight Post Quantum Homomorphic Encryption for Secure Network Traffic Intrusion detection
Abstract
These quantum computing technologies are a serious risk for existing “Cryptographic Algorithms, corresponding “RSA, ECC, and Diffie-Hellman”. The Quantum Algorithm “Shor's can break classical” public-key encryption systems effectively and now there is a momentous security concern of cloud computing, IoT systems, and healthcare networks, as well as the future 6G communication systems. In response to the above difficulties, researchers have suggested two new methods of privacy-preserving encrypted computation, namely: “Post-Quantum Cryptography (PQC) and Fully Homomorphic Encryption (FHE)". But the current “Post-Quantum Homomorphic Encryption (PQHE)” solutions have high computational complexity, expanded ciphertext size, latency, and are not widely deployed in cybersecurity applications. This research aims to provide a lightweight Post-Quantum Homomorphic Encryption framework for secure network traffic analysis with the intrusion detection dataset from CICIDs 2018. The proposed framework combines the lattice-based PQHE concepts, “Principal Component Analysis (PCA) and K-Means cluster to envision encrypted traffic and secure intrusion analysis. Data preprocessing, feature standardization, dimensionality reduction, encrypted traffic representation, clustering analysis and graphical visualization are parts of the experimental methodology. To address the key challenges, by using two major approaches for privacy-preserving encrypted computation have been proposed: “Post-Quantum Cryptography (PQC) and Fully Homomorphic Encryption (FHE)”. The current Post-Quantum Homomorphic Encryption (PQHE) systems, on the contrary, are complicated, costly in the length of the ciphertext, slow and not widely used in terms of cybersecurity. To protect network traffic analysis a lightweight Post-Quantum Homomorphic Encryption framework is proposed in this research to achieve network security using intrusion detection data set CICIDS2018.
References
[2]. Exploring a Decade of Homomorphic Encryption: Advancements, Challenges, and Future Directions 2024
[3]. Bootstrapping in approximate fully homomorphic encryption: a research survey 2025
[4]. Lee J, Duong PN, Lee H. Configurable Encryption and Decryption Architectures for CKKS-Based Homomorphic Encryption. Sensors (Basel). 2023
[5]. TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture 2023
[6]. Experimental Evaluation of Post-Quantum Homomorphic Encryption for Privacy-Preserving I2I Communication in ITS 2025
[7]. From accuracy to approximation: A survey on approximate homomorphic encryption and its applications 2024
[8]. Homomorphic Encryption Based on Lattice Post-Quantum Cryptography 2024
[9]. Post-Quantum Homomorphic Encryption: A Case for Code-Based Alternatives 2025
[10]. An efficient multi-key BFV fully homomorphic encryption scheme with optimized relinearization 2025
[11]. Cheon, J.H., Han, K., Kim, A., Kim, M., Song, Y. (2019). A Full RNS Variant of Approximate Homomorphic Encryption. In: Cid, C., Jacobson Jr., M. (eds) Selected Areas in Cryptography 2019
[12]. Fast and Secure Homomorphic Encryption for Privacy-Preserving Cloud Computation in Post-Quantum IoT 2025
[13]. Encryption and Decryption Technology for Quantum Computing 2024
[14]. Integrating Post-Quantum Cryptography and Advanced Encryption Standards to Safeguard Sensitive Financial Records from Emerging Cyber Threats 2025
[15]. Trustworthy AI Systems Through Lenses of Post-Quantum Security and Privacy-Enhancing Techniques 2026
[16]. Post-Quantum Homomorphic Encryption: A Case for Code-Based Alternatives 2024
[17]. Post-Quantum Security in IoT Aggregation: Future-Proofing Privacy Against Emerging Adversaries 2024
[18]. Homomorphic Encryption Based on Post-Quantum Cryptography2023
[19]. Efficient Post-Quantum Pattern Matching on Encrypted Data 2025
[20]. Post-Quantum Security for Trustworthy Artificial Intelligence: An Emerging Frontier 2024
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