1. Home
  2. To appear
  3. Dynamic Authentication and Privacy-Prese ...

Informatica


Dynamic Authentication and Privacy-Preserving Data Trading Scheme with Lattice in Blockchain-Enhanced Smart Logistics
Pub. online: 16 October 2026      Type: Research Article      Open accessOpen Access
Received
1 April 2026
Accepted
1 August 2026
Published
16 October 2026

Abstract

Against the risks of privacy leakage and quantum computing attacks in smart logistics data trading, this paper proposes a post-quantum privacy-preserving data trading scheme built on consortium blockchain and federated learning. Three orthogonal core innovations are elaborated as follows:
  • (1) A five-layer decoupled smart logistics data trading platform architecture is constructed. Different from existing single-layer blockchain trading frameworks, this work hierarchically divides logistics data workflows into perception, generation, transmission, trading platform and application layers, and introduces a blockchain oracle module to realize consistent synchronization between on-chain transaction records and off-chain IPFS raw cipher storage, solving the cross-layer data inconsistency defect of prior monolithic architectures.
  • (2) A quantitative model parameter quality evaluation algorithm based on cosine similarity, together with a dual-weight dynamic incentive mechanism, is designed. Most existing federated data trading studies lack standardized quantitative quality metrics and static reward allocation rules; our method extracts gradient feature vectors from local FL models, calculates similarity against benchmark parameters to grade data quality, and distributes transaction revenue weighted by both model quality and service participation time to motivate sustained user contribution.
  • (3) A complete lattice-based cryptographic suite including homomorphic proxy re-encryption and aggregate lattice signature is deployed for secure data circulation. Traditional pairing-based privacy schemes are vulnerable to quantum cracking, while standalone lattice encryption works cannot support dynamic cross-buyer ciphertext conversion; our lattice primitive supports homomorphic aggregation and one-time re-encryption transfer, realizing anti-quantum, flexible authorization of federated model trading ciphertexts. Theoretical security proofs and multi-dimensional simulation experiments verify that the proposed scheme achieves post-quantum privacy protection, accurate data quality quantification and high-efficiency dynamic logistics data transactions.

References

 
Abdallah, A., Shen, X.S. (2016). A lightweight lattice-based homomorphic privacy-preserving data aggregation scheme for smart grid. IEEE Transactions on Smart Grid, 9(1), 396–405.
 
Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro, A., Enyeart, D., Ferris, C., Laventman, G., Manevich, Y., Muralidharan, S., Murthy, C., Nguyen, B., Sethi, M., Singh, G., Smith, K., Sorniotti, A., Stathakopoulou, C., Vukolić, M., Weed Cocco, S., Yellick, J. (2018). Hyperledger fabric: a distributed operating system for permissioned blockchains. Proceedings of the Thirteenth EuroSys Conference, 30, 1–15.
 
Azcoitia, S.A., Laoutaris, N. (2022). A survey of data marketplaces and their business models. ACM SIGMOD Record, 51(3), 18–29.
 
Chen J., Xue, J., Wang, Y., Huang, L., Baker, T., Zhou, Z. (2023). Privacy-preserving and traceable federated learning for data sharing in industrial IoT applications. Expert Systems with Applications, 213, 119036.
 
Chen, S., Mi, H., Ping, J., Yan, Z., Shen, Z., Liu, X., Zhang, N., Xia, Q., Kang, C. (2022). A blockchain consensus mechanism that uses proof of solution to optimize energy dispatch and trading. Nature Energy, 7, 495–502.
 
General Data Protection Regulation (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC [Online]. Available. https://eur-lex.europa.eu/legalcontent/EN/TXT/PDF/?uri=CELEX:32016R0679.
 
Gorbunov, S., Vaikuntanathan, V., Wichs, D. (2015). Leveled fully homomorphic signatures from standard lattices. In: Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing, pp. 469–477.
 
Hong, Z., Peng, Z., Zhang, L. (2022). Game analysis on the choice of emission trading among industrial enterprises driven by data. Energy, 239, 122447.
 
Khezr, S., Yassine, A., Benlamri, R., Hossain, M.S. (2022). An edge intelligent blockchain-based reputation system for IIoT data ecosystem. IEEE Transactions on Industrial Informatics, 18(11), 8346–8355.
 
Li, K. (2024a). Privacy-preserving scheme with bidirectional option for blockchain-enhanced logistics internet of things. IEEE Internet of Things Journal, 11(11), 20562–20574.
 
Li, K. (2024b). A novel privacy-preserving range query scheme with permissioned blockchain for smart grid. IEEE Transactions on Emerging Topics in Computing, 13(1), 105–118.
 
Li, K. (2025). Privacy-preserving scheme with smart contracts and quadtree in 5G for fog-enhanced smart logistics. IEEE Internet of Things Journal, 15, 2101–2115.
 
Li, K., Shi, Y. (2024). Toward incentive with privacy preserving machine learning as a service for crowdsensed data trading. IEEE Internet of Things Journal, 11(22), 36494–36507.
 
Li, K., Yang, Y., Wang, S., Shi, R., Li, J. (2021). A lightweight privacy-preserving and sharing scheme with dual-blockchain for intelligent pricing system of smart grid. Computers & Security, 103, 102189.
 
Li, K., Shi, R., Wu, M., Li, Y., Zhang, X. (2022a). A novel privacy-preserving multi-level aggregate signcryption and query scheme for Smart Grid via mobile fog computing. Journal of Information Security and Applications, 67, 103214.
 
Li, K., Yang, Y., Wang, S. (2022b). Privacy-preserving aggregate signcryption scheme with allowing dynamic updating of pseudonyms for fog-based smart grids. Peer-to-Peer Networking and Applications, 15, 2101–2115.
 
Li, K.-C., Shi, R.-H. (2022). A flexible and efficient privacy-preserving range query scheme for blockchain-enhanced IoT. IEEE Internet of Things Journal, 10(1), 720–733.
 
Li, K.-C., Shi, R.-H., Guo, W.-P., Wang, P.-B., Shao, B.-S. (2023). Dynamic range query privacy-preserving scheme for blockchain-enhanced smart grid based on lattice. IEEE Transactions on Dependable and Secure Computing, 21(4), 1652–1664.
 
Lo, S.K. (2022). Toward trustworthy AI: blockchain-based architecture design for accountability and fairness of federated learning systems. IEEE Internet of Things Journal, 10(4), 3276–3284.
 
Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y. (2019). Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Transactions on Industrial Informatics, 16(6), 4177–4186.
 
Luo, F., Al-Kuwari, S., Susilo, W., Duong, D.H. (2020). Chosen-ciphertext secure homomorphic proxy re-encryption. IEEE Transactions on Cloud Computing, 10(4), 2398–2408.
 
Mazzocca, C., Romandini, N., Mendula, M., Montanari, R., Bellavista, P. (2023). TruFLaaS: trustworthy federated learning as a service. IEEE Internet of Things Journal, 10(24), 21266–21281.
 
Meng, T., Zhao, Y., Wolter, K., Xu, C.-Z. (2021). On consortium blockchain consistency: a queueing network model approach. IEEE Transactions on Parallel and Distributed Systems, 32(6), 1369–1382.
 
Qi, J., Lin, F., Chen, Z., Tang, C., Jia, R., Li, M. (2022). High-quality model aggregation for blockchain-based federated learning via reputation-motivated task participation. IEEE Internet of Things Journal, 9(19), 18378–18391.
 
Qian, J., Cao, Z., Dong, X., Shen, J., Liu, Z., Ye, Y. (2020). Two secure and efficient lightweight data aggregation schemes for smart grid. IEEE Transactions on Smart Grid, 12(3), 2625–2637.
 
Qian, J., Cao, Z., Lu, M., Chen, X., Shen, J., Liu, J. (2021). The secure lattice-based data aggregation scheme in residential networks for smart grid. IEEE Internet of Things Journal, 9(3), 2153–2164.
 
Riley, G.F., Henderson, T.R. (2010). The ns-3 network simulator. In: Modeling and Tools for Network Simulation, Springer Berlin Heidelberg, Berlin, Heidelberg.
 
Tu, X., Zhu, K., Cong Luong, N., Niyato, D., Zhang, Y., Li, J. (2022). Incentive mechanisms for federated learning: from economic and game theoretic perspective. IEEE Transactions on Cognitive Communications and Networking, 8(3), 1566–1593.
 
Wang, X., Zhao, Y., Qiu, C., Liu, Z., Nie, J., Leung, V.C.M. (2022). InFEDge: a blockchain-based incentive mechanism in hierarchical federated learning for end-edge-cloud communications. IEEE Journal on Selected Areas in Communications, 40(12), 3325–3342.
 
Wang, Y., Peng, H., Su, Z., Luan, T.H., Benslimane, A., Wu, Y. (2022). A platform-free proof of federated learning consensus mechanism for sustainable blockchains. IEEE Journal on Selected Areas in Communications, 40(12), 3305–3324.
 
Warnat-Herresthal, S., Schultze, H., Shastry, K.L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Hándler, K., Pickkers, P., Ahmad Aziz, N., Ktena, S., Tran, F., Bitzer, M., Ossowski, S., Casadei, N., Herr, C., Petersheim, D., Behrends, U., Kern, F., Fehlmann, T., Schommers, P., Lehmann, C., Augustin, M., Rybniker, J. (2021). Swarm learning for decentralized and confidential clinical machine learning. Nature, 594, 265–270.
 
Xu, G., Zhou, Z., Dong, J., Zhang, L., Song, X. (2023). A blockchain-based federated learning scheme for data sharing in industrial internet of things. IEEE Internet of Things Journal, 10(24), 21467–21478.
 
Yang, F., Qiao, Y., Zoynul Abedin, M., Huang, C. (2022). Privacy-preserved credit data sharing integrating blockchain and federated learning for industrial 4.0. IEEE Transactions on Industrial Informatics, 18(12), 8755–8764.
 
Yang, Z., Shi, Y., Zhou, Y., Wang, Z., Yang, K. (2022). Trustworthy federated learning via blockchain. IEEE Internet of Things Journal, 10(1), 92–109.
 
Ye, J. (2011). Cosine similarity measures for intuitionistic fuzzy sets and their applications. Mathematical and Computer Modelling, 53, 91–97.
 
Zhang, H., Hua, G., Wang, X., Jiang, H., Yang, W. (2023). Categorical inference poisoning: verifiable defense against black-Box DNN model stealing without constraining surrogate data and query times. IEEE Transactions on Information Forensics and Security, 18, 1473–1486.
 
Zhang, J., Liu, Y., Qin, X., Xu, X., Zhang, P. (2023). Adaptive resource allocation for blockchain-based federated learning in internet of things. IEEE Internet of Things Journal, 10(12), 10621–10635.
 
Zhou, Z., Tian, Y., Xiong, J., Ma, J., Peng, C. (2022). Blockchain-enabled secure and trusted federated data sharing in IIoT. IEEE Transactions on Industrial Informatics, 19(5), 6669–6681.

Biographies

Gao Xue
gaoxue1026@ncepu.edu.cn

X. Gao received the master’s degree from Shandong University of Science and Technology, Qingdao, China, in 2019. She is currently pursuing the PhD degree with North China Electric Power University, Beijing. Her current research interest includes privacy protection and identity authentication in Power Internet of Things.

Wu Ke-He
wkh@ncepu.edu.cn

K.-H. Wu received a PhD degree from the University of North China Electric Power University, Beijing, in 2009. He is a professor at North China Electric Power University, the director of the Chinese Association for Artificial Intelligence and Beijing Engineering Research Center of Electric Information Technology, and a committee member of the China Electric Power Information Standardization Committee and Professional Electric Power Information Committee of the Chinese Society for Electrical Engineering. His research interests include smart grid software technology, power information security, and deep learning.

Zhang Xiao-Liang
zhanghino@126.com

X.-L. Zhang received a PhD degree from North China Electric Power University, Beijing, in 2023. He is a senior experimentalist at North China Electric Power University. His research interests include electric power information security, and artificial intelligence.

Cui Wen-Chao
cuzz@ncepu.edu.cn

W.-C. Cui received his PhD from North China Electric Power University, Beijing, China, in 2014. His research interest focuses on power information security, security protection for Power Internet of Things.


Full article PDF XML
Full article PDF XML

Copyright
© 2026 Vilnius University
Open access article under the CC BY license.

Keywords
Index Terms—smart logistics data trading lattice cryptography federated learning consortium blockchain post-quantum privacy preservation

Funding
This research is funded by the National Key R&D Program of China, grant number 2020YFB0905900.

Metrics
since January 2020
60

Article info
views

24

Full article
views

16

PDF
downloads

20

XML
downloads


Share


RSS