This thesis explores the development of a blockchain-based privacy-preserving automotive insurance system. It investigates the integration of Privacy-Enhancing Technologies (PETs), cryptographic methods, and permissioned blockchain frameworks to ensure security, transparency, and regulatory compliance in Usage-Based Insurance (UBI) models. The proposed solution leverages Homomorphic Encryption (HE) for secure computations on encrypted data and Verifiable Decryption (VD) to establish trust without exposing raw data. Through the implementation of Hyperledger Fabric (HLF), the system achieves privacy-preserving premium calculation, enhancing fairness and user trust. The results demonstrate the feasibility of privacy-centric automotive insurance solutions that align with modern regulatory frameworks.