Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 10, Issue 1, 2026

Design of Privacy Protection Mechanism for Federated Learning Oriented to Data Security

Download as PDF

DOI: 10.23977/acss.2026.100120 | Downloads: 7 | Views: 107

Author(s)

Haoran Huang 1

Affiliation(s)

1 Sun Yat-sen University, Guangzhou, Guangdong, 510275, China

Corresponding Author

Haoran Huang

ABSTRACT

Federated Learning (FL) enables collaborative model training across decentralized clients without exposing raw data but remains vulnerable to privacy attacks such as gradient leakage, model inversion, and membership inference. This paper proposes Hybrid Shield for Federated Learning, a multi-layer protection mechanism combining adaptive differential privacy, selective homomorphic encryption, and robust aggregation to deliver configurable privacy guarantees. Extensive experiments on MNIST, CIFAR-10, and Fashion-MNIST under IID and Non-IID settings show that the mechanism reduces attack success rates by up to 94.7% while keeping model accuracy within 2.3% of the unprotected baseline. Adaptive noise injection balances privacy and utility based on client heterogeneity and network conditions, while selective encryption cuts computational overhead by 42.6% compared to full homomorphic encryption. This work offers a practical, scalable solution for privacy-preserving FL in data-sensitive applications.

KEYWORDS

Federated learning; privacy protection; differential privacy; homomorphic encryption; gradient leakage attack

CITE THIS PAPER

Haoran Huang. Design of Privacy Protection Mechanism for Federated Learning Oriented to Data Security. Advances in Computer, Signals and Systems (2026). Vol. 10, No. 1, 170-177. DOI: http://dx.doi.org/10.23977/acss.2026.100120.

REFERENCES

[1] Li Q, Wen Z, Wu Z, et al. A survey on federated learning systems: Vision, hype and reality for data privacy and protection[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 35(4): 3347-3366.
[2] Hasan M T, Kudapa S P. Data privacy-aware machine learning and federated learning: A framework for data security[J]. American Journal of Interdisciplinary Studies, 2021, 2(03): 01-34.
[3] Qu Z, Tang Y, Muhammad G, et al. Privacy protection in intelligent vehicle networking: A novel federated learning algorithm based on information fusion[J]. Information Fusion, 2023, 98: 101824.
[4] Kim S. Incentive design and differential privacy based federated learning: A mechanism design perspective[J]. IEEE Access, 2020, 8: 187317-187325.
[5] Nguyen T, Thai M T. Preserving privacy and security in federated learning[J]. IEEE/ACM Transactions on Networking, 2023, 32(1): 833-843.
[6] Gong C, Zhang X, Lin Y, et al. Federated learning for heterogeneous data integration and privacy protection[C]//2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025: 459-466.
[7] Awan K A, Din I U, Almogren A, et al. Privacy-preserving big data security for IoT with federated learning and cryptography[J]. IEEE access, 2023, 11: 120918-120934.
[8] Li Z, Sharma V, Mohanty S P. Preserving data privacy via federated learning: Challenges and solutions[J]. IEEE Consumer Electronics Magazine, 2020, 9(3): 8-16.
[9] Sun Z, Xu J, Li J, et al. Privacy protection authentication protocol for consumer Internet of Things in horizontal federated learning environment[J]. IEEE Transactions on Consumer Electronics, 2025, 71(4): 10551-10560.
[10] Yue Y, Ming Z, Zhijie Q, et al. A data protection-oriented design procedure for a federated learning framework[C]//2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020: 968-974.
[11] Cheng H, Lu T, Hao R, et al. Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection[J]. Applied Energy, 2024, 358: 122570.
[12] Sandeepa C, Siniarski B, Wang S, et al. Rec-Def: A recommendation-based defence mechanism for privacy preservation in federated learning systems[J]. IEEE Transactions on Consumer Electronics, 2023, 70(1): 2716-2728.
[13] Wen J, Zhang Z, Lan Y, et al. A survey on federated learning: challenges and applications[J]. International journal of machine learning and cybernetics, 2023, 14(2): 513-535.
[14] Zhang J, Zhu H, Wang F, et al. Security and privacy threats to federated learning: Issues, methods, and challenges[J]. Security and Communication Networks, 2022, 2022(1): 2886795.
[15] Hao M, Li H, Luo X, et al. Efficient and privacy-enhanced federated learning for industrial artificial intelligence[J]. IEEE Transactions on Industrial Informatics, 2019, 16(10): 6532-6542.
[16] Manzoor H U, Shabbir A, Chen A, et al. A survey of security strategies in federated learning: Defending models, data, and privacy[J]. Future Internet, 2024, 16(10): 374.
[17] Xiong Z, Cai Z, Takabi D, et al. Privacy threat and defense for federated learning with non-iid data in AIoT[J]. IEEE Transactions on Industrial Informatics, 2021, 18(2): 1310-1321.
[18] Guo X. Federated learning for data security and privacy protection[C]//2021 12th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). IEEE, 2021: 194-197.
[19] Chen C, Liu J, Tan H, et al. Trustworthy federated learning: privacy, security, and beyond[J]. Knowledge and Information Systems, 2025, 67(3): 2321-2356.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 46386
Visits: 1053812

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.