基于矩阵加密的无人机群位置隐私保护方法

邢玲; 贾晓凡; 赵鹏程; 吴红海

doi:10.7527/S1000-6893.2021.25386

航空学报 >

2022 , Vol. 43 >Issue 8: 325386 - 325386

DOI: https://doi.org/10.7527/S1000-6893.2021.25386

电子电气工程与控制

基于矩阵加密的无人机群位置隐私保护方法

邢玲 ,
贾晓凡 ,
赵鹏程 ,
吴红海

展开

河南科技大学信息工程学院, 洛阳 471023

收稿日期: 2021-02-05

修回日期: 2021-06-29

网络出版日期: 2021-06-29

基金资助

国家自然科学基金(62071170,62072158);河南省杰出青年科学基金(222300420006);河南省高校科技创新团队支持计划资助(21IRTSTHN015)

收起

Location privacy protection scheme for unmanned aerial vehicle group based on matrix encryption

XING Ling ,
JIA Xiaofan ,
ZHAO Pengcheng ,
WU Honghai

Expand

School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China

Received date: 2021-02-05

Revised date: 2021-06-29

Online published: 2021-06-29

Supported by

National Natural Science Foundation of China (62071170,62072158);Henan Province Science Fund for Distinguished Young Scholars (222300420006);Program for Innovative Research Team in University of Henan Province (21IRTSTHN015)

Fold

摘要

空天密集组网将传统单一维度的地面网络扩展为空天平台协同组网的立体化网络,无人机群组技术为复杂区域全覆盖提供了新的技术手段,但信息传输过程中的隐私与安全问题日渐突出。针对无人机群采集信息时的位置隐私信息泄露问题,提出基于矩阵加密的位置隐私保护方法。利用云服务器的通信功能将其作为可信第三方,进行基站和无人机间信息的双向传输,并利用其计算功能对任务重新分配、加密传输;基于k-匿名思想对无人机身份和任务进行双重匿名,降低攻击者对无人机位置信息的预测;通过任务列表追踪无人机群组内恶意节点并进行身份撤销,以抵御群组内部攻击。通过理论和实验分析,使用该方法时任务执行率超过80%,ID和任务匿名熵随着群组数量的增多而变大,验证了方法的可行性与有效性,表明该方法能够为无人机位置隐私提供有效的保护。

关键词： 空天密集组网; 无人机; 群组; 云计算; 位置隐私保护

本文引用格式

邢玲 , 贾晓凡 , 赵鹏程 , 吴红海 . 基于矩阵加密的无人机群位置隐私保护方法[J]. 航空学报, 2022 , 43(8) : 325386 -325386 . DOI: 10.7527/S1000-6893.2021.25386

Abstract

The space-aerial intensive network expands the traditional single-dimensional terrestrial network into a three-dimensional aerospace collaborative network, and the Unmanned Aerial Vehicle (UAV) group technology has become a new technical means for full coverage of complex areas. However, the issue of privacy and security in the processes of information transmission is becoming increasingly prominent. In this paper, we propose a privacy protection method based on matrix encryption, in order to solve the problem that the location privacy is easy to leak when the UAV group collects information. The cloud server because of the communication function is regarded as a trusted third party to carry out two-way information transfer between UAVs and the base stations. Taking advantage of its computing capabilities, the cloud server is used to encrypt information transfer and reallocate tasks randomly. The IDs and tasks are hidden and encrypted doubly according to the k-anonymous algorithm, so as to reduce the attackers’ guesses on the target location information. The algorithm can help the system fend off attacks within the group, because the cloud server can track the target and cancel the identity from the new task list when a malicious node is found. The entropy of IDs and tasks increase as the number of groups increases. Feasibility and effectiveness of the algorithm are verified, which shows that the algorithm can provide effective protection for the location privacy of UAVs.

Key words： space-aerial intensive network; unmanned aerial vehicle; group; cloud computing; location privacy protection

参考文献

[1] 梁彦霞, 姜静, 孙长印, 等. 超密集组网下一种基于干扰增量降低的分簇算法[J]. 电子与信息学报, 2020, 42(2): 495-502. LIANG Y X, JIANG J, SUN C Y, et al. A cluster algorithm based on interference increment reduction in ultra-dense network[J]. Journal of Electronics & Information Technology, 2020, 42(2): 495-502 (in Chinese).
[2] LUO S, XIAO Y, LIN R P, et al. Opportunistic spectrum access for UAV communications towards ultra dense networks[J]. IEEE Access, 2019, 7: 175021-175032.
[3] 范茜莹, 黄传河, 朱钧宇, 等. 无人机辅助车联网环境下干扰感知的节点接入机制[J]. 通信学报, 2019, 40(6): 90-101. FAN X Y, HUANG C H, ZHU J Y, et al. Interference-aware node access scheme in UAV-aided VANET[J]. Journal on Communications, 2019, 40(6): 90-101 (in Chinese).
[4] WINKELMAIER G, BATTULWAR R, KHOSHDELI M, et al. Topographically guided UAV for identifying tension cracks using image-based analytics in open-pit mines[J]. IEEE Transactions on Industrial Electronics, 2021, 68(6): 5415-5424.
[5] SHARMA A, VANJANI P, PALIWAL N, et al. Communication and networking technologies for UAVs: a survey[J]. Journal of Network and Computer Applications, 2020, 168: 102739.
[6] 刘贞报, 马博迪, 高红岗, 等. 基于形态自适应网络的无人机目标跟踪方法[J]. 航空学报, 2021, 42(4): 524904. LIU Z B, MA B D, GAO H G, et al. Adaptive morphological network based UAV target tracking algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524904 (in Chinese).
[7] 江波, 屈若锟, 李彦冬, 等. 基于深度学习的无人机航拍目标检测研究综述[J]. 航空学报, 2021, 42(4): 524519. JIANG B, QU R K, LI Y D, et al. Object detection in UAV imagery based on deep learning: Review[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524519 (in Chinese).
[8] CARDOSO D N, ESTEBAN S, RAFFO G V. A new robust adaptive mixing control for trajectory tracking with improved forward flight of a tilt-rotor UAV[J]. ISA Transactions, 2021, 110: 86-104.
[9] ZHI Y Y, FU Z J, SUN X M, et al. Security and privacy issues of UAV: a survey[J]. Mobile Networks and Applications, 2020, 25(1): 95-101.
[10] WANG H C, WANG J L, DING G R, et al. Robust spectrum sharing in air-ground integrated networks: opportunities and challenges[J]. IEEE Wireless Communications, 2020, 27(3): 148-155.
[11] FOTOUHI A, QIANG H R, DING M, et al. Survey on UAV cellular communications: practical aspects, standardization advancements, regulation, and security challenges[J]. IEEE Communications Surveys & Tutorials, 2019, 21(4): 3417-3442.
[12] SHARMA B, OBAIDAT M S, SHARMA V, et al. Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions[J]. International Journal of Communication Systems, 2020, 33(18): e4628.
[13] MAMAGHANI M T, HONG Y. Improving PHY-security of UAV-enabled transmission with wireless energy harvesting: robust trajectory design and communications resource allocation[J]. IEEE Transactions on Vehicular Technology, 2020, 69(8): 8586-8600.
[14] JIANG C J, FANG Y, ZHAO P H, et al. Intelligent UAV identity authentication and safety supervision based on behavior modeling and prediction[J]. IEEE Transactions on Industrial Informatics, 2020, 16(10): 6652-6662.
[15] WANG W, LI X R, ZHANG M, et al. Energy-constrained UAV-assisted secure communications with position optimization and cooperative jamming[J]. IEEE Transactions on Communications, 2020, 68(7): 4476-4489.
[16] LI R D, WEI Z Q, YANG L, et al. Resource allocation for secure multi-UAV communication systems with multi-eavesdropper[J]. IEEE Transactions on Communications, 2020, 68(7): 4490-4506.
[17] GUAN Z Y, LYU H Z, LI D W, et al. Blockchain: a distributed solution to UAV-enabled mobile edge computing[J]. IET Communications, 2020, 14(15): 2420-2426.
[18] TIAN Y F, YUAN J W, SONG H B. Efficient privacy-preserving authentication framework for edge-assisted Internet of Drones[J]. Journal of Information Security and Applications, 2019, 48: 102354.
[19] SWEENEY L. K-anonymity: a model for protecting privacy[J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10(5): 557-570.
[20] XING L, DENG K K, WU H H, et al. A survey of across social networks user identification[J]. IEEE Access, 2019, 7: 137472-137488.
[21] BROWN L A, SUTHERLAND D H, DASH J. Low-cost unmanned aerial vehicle-based digital hemispherical photography for estimating leaf area index: a feasibility assessment[J]. International Journal of Remote Sensing, 2020, 41(23): 9064-9074.
[22] WEI Z Q, ZHU J L, GUO Z J, et al. The performance analysis of spectrum sharing between UAV enabled wireless mesh networks and ground networks[J]. IEEE Sensors Journal, 2021, 21(5): 7034-7045.
[23] NING Z L, DONG P R, WANG X J, et al. Distributed and dynamic service placement in pervasive edge computing networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2021, 32(6): 1277-1292.
[24] DENG K K, XING L, ZHANG M C, et al. A multiuser identification algorithm based on internet of things[J]. Wireless Communications and Mobile Computing, 2019, 2019: 6974809.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献