Secure Estimation Using Partially Homomorphic Encryption for Unmanned Aerial Systems in the Presence of Eavesdroppers

Xinhao Yan, Guanzhong Zhou, Yue Huang, Wei Meng, Anh Tu Nguyen, Hailong Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

Unmanned aerial systems (UASs) are attracting increasing attention thanks to the great mobility and flexibility of unmanned aerial vehicles (UAVs). This paper considers a typical UAS, which consists of a UAV, a sensing device that provides some sensed data to the UAV, and an end-user that operates the UAV. However, the information exchanged between these parties is vulnerable to eavesdropping attacks, emphasizing the need to develop privacy-preserving approaches. The cryptographic methods are undoubtedly effective, but their high computational overhead may adversely impact the normal operations of UASs. Additionally, the dynamic of a UAV has a high dimension, which is disadvantageous for both estimation and encryption. Therefore, this paper proposes a secure distributed estimation protocol with partially homomorphic encryption by encrypting the transmitted measurements and estimates. Attribute to distributed structure and partial homomorphism, the computation amount for secure estimation is greatly reduced. At the same time, the raw data that needs to be encrypted is transferred into the space of plaintexts by a uniform quantizer and a mapping strategy. Finally, the effectiveness of the proposed method is verified by computer simulation.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Intelligent Vehicles
DOIs
Publication statusAccepted/In press - 2024

Keywords

  • Autonomous aerial vehicles
  • Cryptography
  • Estimation
  • Homomorphic encryption
  • homomorphic encryption
  • Intelligent vehicles
  • Quantization (signal)
  • state estimation
  • uniform quantization
  • Unmanned aerial vehicles (UAVs)
  • Vehicle dynamics

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Optimization
  • Artificial Intelligence

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