NONE 20260421094936490 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON SIGNAL PROCESSING 1790-5052 2224-3488 Michael Sotula Masangu Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF THE CONGO Moanda Ndeko Mosengo C. M. Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF THE CONGO Witesyavwirwa Vianney Kambale Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria, SOUTH AFRICA Kyandoghere Kyamakya Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF THE CONGO Fully Homomorphic Encryption (FHE) enables mathematical operations directly on encrypted data without decryption. Any operation that a polynomial can approximate can, in principle, be executed under an FHE scheme. To protect cyber-physical systems from eavesdropping on sensitive measurements, we integrate CKKS, an FHE scheme for encrypted real and complex arithmetic, into a state estimator. The estimator is an Extended Kalman Filter (EKF) that fuses GPS and Inertial Measurement Unit (IMU) data to estimate vehicle position, velocity, linear acceleration, yaw angle, and turn rate. We implement CKKS using the Microsoft SEAL library, which supports only a limited number of homomorphic arithmetic operations, creating major challenges for EKF steps such as matrix inversion. We address these constraints with operation-efficient approximations and structured compromises. Frobenius norm analysis shows that the encrypted EKF preserves the precision of the plaintext EKF while reducing data exposure, at the cost of increased latency. 04 21 2026 04 21 2026 108 9 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232014.2026.22.9 https://wseas.com/journals/sp/2026/a185114-008(2026).pdf Rasim Alguliyev, Yadigar Imamverdiyev, and Lyudmila Sukhostat, “Cyber-physical systems and their security issues,” Computers in Industry, vol. 100, pp. 212–223, 2018. DOI: 10.1016/j.compind.2018.04.017. Abdul Rahman Sani, Muneeb Ul Hassan, and Jinjun Chen, “Privacy preserving machine learning for electric vehicles: A survey,” arXiv preprint arXiv:2205.08462, 2022. DOI: 10 . 48550/arXiv.2205.08462. Greg Welch and Gary Bishop, An introduction to the kalman filter, https://www.cs.unc. edu/~welch/media/pdf/kalman_intro. pdf, Access Date: 28/11/2024. 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