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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

Enabling Role of Post-Quantum Secure Multi-Party Private Intersection Protocol in Federated Learning

Xihan Zhao

Published Nov 28, 2025• 0 citations
Publisher

Abstract

With the rapid development of quantum computing technology, traditional encryption methods face severe security threats in multi-party privacy intersection protocols in federated learning. In this paper, we propose a new protocol based on post-quantum cryptography. Firstly, lattice-based homomorphic encryption and zero-knowledge proof technology are used to achieve key generation and parameter initialization against quantum attacks. Secondly, ciphertext data encoding is carried out to support homomorphic operations. Next, a zero-knowledge proof is used to verify the correctness of the ciphertext intersection calculation. Finally, the protocol is embedded into the federated learning workflow, adaptively adjusting the parameters. Experimental results show that the protocol achieves the NIST (National Institute of Standards and Technology) security level 3, and the privacy leakage rate is less than 1.2%, the communication and computational costs are controllable, and the protocol does not bring great influence to the accuracy of the federated learning model. The experimental results verify that the protocol can provide a reliable protection for the privacy of federated learning data in the quantum era.

DOI
10.1109/auteee67053.2025.11322388

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With the rapid development of quantum computing technology, traditional encryption methods face severe security threats in multi-party privacy intersection protocols in federated learning. In this paper, we propose a new protocol based on post-quantum cryptography. Firstly, lattice-based homomorphic encryption and zero-knowledge proof technology are used to achieve key generation and parameter initialization against quantum attacks. Secondly, ciphertext data encoding is carried out to support homomorphic operations. Next, a zero-knowledge proof is used to verify the correctness of the ciphertext intersection calculation. Finally, the protocol is embedded into the federated learning workflow, adaptively adjusting the parameters. Experimental results show that the protocol achieves the NIST (National Institute of Standards and Technology) security level 3, and the privacy leakage rate is less than 1.2%, the communication and computational costs are controllable, and the protocol does not bring great influence to the accuracy of the federated learning model. The experimental results verify that the protocol can provide a reliable protection for the privacy of federated learning data in the quantum era.
Federated learning is a privacy-preserving machine learning paradigm consisting of local model training, global model aggregation, and global model distribution. Recent research found that model aggregation can reveal the participants’ privacy. To preserve the participants’ privacy, multi-party computation-based secure aggregation is used in federated learning with mobile device participants. The character of the mobile device requires that secure aggregation can be efficient in computation and robust to the dropout. However, prior works need multi rounds, increase computation cost related to the dropped participants, and fails to resist quantum attacks. To solve these issues, we propose a 3-round post-quantum secure protocol for federated learning. In the proposed protocol, single-masking generated by homomorphic Pseudorandom Generator based on learning with round encrypts single user's model. After all the encrypted models are aggregated fast, additively homomorphic decryption based on Shamir secret share guarantee the robustness and performance induced by the dropped participants. All message exchange is based on a post-quantum secure channel constructed with the first post-quantum cryptography standard, Kyber KEM. In post-quantum security, security analysis demonstrates that the proposed protocol can preserve privacy under the sem-honest adversaries setting, and the experimental results show higher running time efficiency.
As quantum computing technology continues to advance, the development of post-quantum cryptography (PQC) methods capable of resisting quantum computing attacks has emerged as a key research direction. Existing homomorphic encryption schemes, such as RSA, ElGamal, and Paillier, face potential vulnerabilities to quantum attacks. To address this challenge, this study proposes a lattice-based PQC homomorphic encryption scheme. The approach leverages lattice cryptographic methods to resist quantum attacks while enabling homomorphic encryption for computational applications. This research provides mathematical proofs, computational examples, and a comprehensive security analysis of the proposed lattice-based PQC homomorphic encryption scheme. The findings aim to serve as a reference for developers working on homomorphic encryption applications, such as federated learning.
Collaborative threat intelligence via federated learning (FL) faces critical risks from quantum computing, which can compromise classical encryption methods. This study proposes a quantum-secure FL framework using post-quantum cryptography (PQC) to protect cross-organizational data sharing. We expose vulnerabilities in traditional FL through simulated quantum attacks on RSA encrypted gradients and introduce a hybrid architecture integrating NIST-standardized algorithms CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication. Testing on APT attack datasets demonstrated 97.6% threat detection accuracy with minimal latency overhead (18.7%), validating real-world viability. A healthcare consortium case study confirmed secure ransomware indicator sharing without breaching privacy regulations. The work highlights the urgency of quantum ready defenses and provides technical guidelines for deploying PQC in FL systems, alongside policy recommendations for standardizing quantum resilience in threat-sharing networks.
Federated Learning (FL) enables collaborative machine learning without centralizing raw data. However, the impending threat of cryptographically-relevant quantum computers exposes FL's reliance on classical public-key cryptography (e.g., RSA, ECC) to Harvest-Now-Decrypt-Later (HNDL) attacks. We present PQ-FL, the first open federated learning control plane secured end-to-end using the finalized NIST Post-Quantum Cryptography (PQC) standards. Our architecture integrates ML-DSA-87 (FIPS 204) for transport-layer identity authentication (mTLS) and ML-KEM-1024 (FIPS 203) for application-layer model payload encryption. To overcome the lack of comprehensive PQC support in standard Python libraries (BoringSSL), we introduce a novel OQS-stunnel sidecar pattern that proxies edge client traffic. We evaluate the system on a CUDA-accelerated edge cluster training a convolutional neural network. Empirical results confirm successful multi-round aggregation using FedAvg, Krum, and Trimmed-Mean strategies alongside differential privacy, achieving seamless PQC key encapsulation (1568-byte ciphertexts) and verifiable asynchronous global model streaming.
Abstract Quantum learning models hold the potential to bring computational advantages over the classical realm. As powerful quantum servers become available on the cloud, ensuring the protection of clients’ private data becomes crucial. By incorporating quantum homomorphic encryption schemes, we present a general framework that enables quantum delegated and federated learning with a computation-theoretical data privacy guarantee. We show that learning and inference under this framework feature substantially lower communication complexity compared with schemes based on blind quantum computing. In addition, in the proposed quantum federated learning scenario, there is less computational burden on local quantum devices from the client side, since the server can operate on encrypted quantum data without extracting any information. We further prove that certain quantum speedups in supervised learning carry over to private delegated learning scenarios employing quantum kernel methods. Our results provide a valuable guide toward privacy-guaranteed quantum learning on the cloud, which may benefit future studies and security-related applications.
The intensive development of smart city cyber-infrastructure has aggravated the security issues because of the heterogeneity of devices, the volume of constantly changing data, and the lack of confidentiality in the long term. Traditional public-key cryptographic systems like RSA and ECC are becoming susceptible to quantum-powered attacks and are inappropriate to use in smart city applications in the future. Despite their quantum resistance, post-quantum cryptographic (PQC) schemes are not widely used in practice due to high computational cost, difficulty in key management, and inflexibility to dynamic cyber-threats. Furthermore, the current security systems do not tend to be decentralized, but the sources of intelligence are usually centralized which creates issues of privacy and scalability. This paper mitigates these shortages by suggesting a safe post-quantum architecture that incorporates an adjustable federated learning architecture and a hybridized lattice-based Key Encapsulation Mechanism to smart city cyber-infrastructure. The KEM based on lattice provides resistant to quantum key exchange, whereas federated learning provides decentralized threat intelligence without sensitive local information disclosure. An adaptive aggregation approach dynamically scales model updates based on node reliability and threat intensity, improving resilience to poisoning and inference attacks. Benchmark post-quantum parameters and public intrusion datasets, such as UNSW-NB15 and CICIDS2017 are tested in the framework in a simulated smart city setting. The experimental findings indicate a maximum common exchange latency reduction of 21 percent, versus standalone lattice schemes, and a threat detection rate of 97.8 percent with stable convergence. The results have shown the efficiency of integrating post-quantum cryptography with adaptive federated intelligence in ensuring the security of the next generation smart city infrastructures.