Research on privacy protection of dynamic selective homomorphic encryption in the context of UAV federated learning

QIU Jianbin; ZHANG Zuwei; ZHENG Yuhui

doi:10.11959/j.issn.1000-0801.2025224

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Research on privacy protection of dynamic selective homomorphic encryption in the context of UAV federated learning

Research and Development | 更新时间：2026-01-08

- Research on privacy protection of dynamic selective homomorphic encryption in the context of UAV federated learning
- Telecommunications Science Vol. 41, Issue 12, Pages: 116-127(2025)
- 作者机构：
  
  1.南京工业大学信息管理中心，江苏南京 211816
  2.南京工业大学计算机与信息工程学院（人工智能学院），江苏南京 211816
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025224
  CLC： TP18;TN918
- Received：16 June 2025，
  
  Revised：2025-07-03，
  
  Accepted：10 July 2025，
  
  Published：20 December 2025
- 稿件说明：
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仇建斌,章祖葳,郑宇辉.无人机联邦学习场景下动态选择性同态加密隐私保护研究[J].电信科学,2025,41(12):116-127.

QIU Jianbin,ZHANG Zuwei,ZHENG Yuhui.Research on privacy protection of dynamic selective homomorphic encryption in the context of UAV federated learning[J].Telecommunications Science,2025,41(12):116-127.
仇建斌,章祖葳,郑宇辉.无人机联邦学习场景下动态选择性同态加密隐私保护研究[J].电信科学,2025,41(12):116-127. DOI： 10.11959/j.issn.1000-0801.2025224.

QIU Jianbin,ZHANG Zuwei,ZHENG Yuhui.Research on privacy protection of dynamic selective homomorphic encryption in the context of UAV federated learning[J].Telecommunications Science,2025,41(12):116-127. DOI： 10.11959/j.issn.1000-0801.2025224.

摘要

随着低空经济的快速发展，无人机（unmanned aerial vehicle，UAV）在环境监测、应急救援和物流配送等领域得到广泛应用，并在数据采集与处理过程中面临日益突出的隐私保护需求。为了应对这一挑战，联邦学习结合同态加密被引入以提升数据安全性，但在计算与通信资源受限的无人机场景下，其高昂的资源开销成为实际应用的主要瓶颈。为此，提出了一种面向无人机场景的选择性同态加密隐私保护方案。在每轮本地训练后，客户端基于梯度敏感度评估参数重要性，并结合通信与能耗预算，通过启发式贪心算法动态选择“隐私收益”最大的参数子集进行加密。该方案在联邦学习框架下实现，并采用CKKS同态加密库进行模拟实验。基于CIFAR-10数据集和SimpleCNN模型，对比了5种方案：无加密、全加密、MaskCrypt固定比例加密、DP-AvgFed方案及提出的动态预算方案。实验结果表明，所提方法在实现与MaskCrypt相当安全性的同时，资源开销降低约10%，在保障隐私的同时有效控制了资源消耗，验证了其在资源受限的无人机场景中的可行性和优越性。

Abstract

With the rapid development of the low-altitude economy

unmanned aerial vehicle (UAV) has been widely used in areas such as environmental monitoring

emergency rescue

and logistics delivery. As UAV increasingly engage in data collection and processing

the demand for privacy protection in such scenarios has become increasingly prominent. To address this issue

federated learning combined with homomorphic encryption has been adopted to enhance data security. However

under the constraints of limited computational and communication resources on UAV

the high resource overhead of such approaches becomes a major bottleneck for practical deployment. To this end

a selective homomorphic encryption scheme for privacy protection tailored to UAV scenarios was proposed. After each round of local training

the client evaluates the importance of model parameters based on gradient sensitivity was evaluated by client

and within the given communication and energy budget

dynamically a subset of parameters was selected with the highest “privacy gain” for encryption via a heuristic greedy algorithm. The scheme was implemented within a federated learning framework and simulated using the CKKS homomorphic encryption library. Experiments were conducted on the CIFAR-10 dataset using the SimpleCNN model

comparing five schemes: no encryption

full encryption

fixed-ratio MaskCrypt

DP-AvgFed scheme and the proposed dynamic budget scheme. Results show that the proposed method achieves a balanced trade-off between resource consumption and security. Compared to MaskCrypt

it achieves comparable privacy protection with approximately 10% lower resource overhead

demonstrating its feasibility and effectiveness in resource-constrained UAV scenarios.

关键词

Keywords

references

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