## 9 Discussion
### Privacy Preservation Techniques in Vehicular Communications  
Considering the increasing popularity of vehicular networks, privacy preservation emerges as a vital concern, specifically in transactions between autonomous vehicles and third-party entities, such as traffic management systems. The following methods underscore the key techniques employed to uphold the privacy of information within these interactions:  
**Encryption:** As a foundational method in information security, encryption transforms data into a code to impede unauthorized access. Various algorithms--both symmetric (e.g., AES and DES) and asymmetric (e.g., RSA and ECC)--are employed to safeguard sensitive data [184]. **Secure communication protocols:** During data transit, the adoption of secure communication protocols is integral. Protocols such as transport layer security (TLS) and Internet protocol security (IPSec) deploy robust encryption and authentication mechanisms to maintain data confidentiality and integrity [185, 186]. **Anonymization:** To protect the identity of individuals or vehicles, anonymization techniques are used. These involve the removal or encryption of personally identifiable information (PII) such as location, image, and license number [187]. For instance, pseudonym systems can be employed where vehicles are allocated pseudonyms, obscuring the traceability of the vehicles' real identity. **Blockchain technology and blockchain smart contract code:** The decentralized nature of blockchain technology fortifies data privacy within vehicular networks. It ensures that every data transaction is recorded and authenticated transparently and securely, prohibiting unauthorized alteration or access. In [188], the authors reviewed the applied state-of-the-art formal methods of smart contract specification and verification with the aim of reducing the risk of faults and bug occurrence and avoiding possible resulting costs. However, most approaches fail to reflect the characteristics of the blockchain and user behavior. In, [189], the authors proposed a novel formal modeling approach to verify the execution environment behavior of smart contracts. The authors applied this formalization to a real-world example of a smart contract and analyzed its violations using a statistical model verification technique [189]. **Differential Privacy:** This methodology allows the public dissemination of information about patterns within a dataset, while safeguarding the individual data points within. In the context of vehicular networks, differential privacy can be leveraged when sharing data with third parties, thereby preventing any possibility of reverse engineering to single out individual vehicles or users. **Homomorphic encryption:** This method allows for computations to be conducted on encrypted data without the necessity of decryption. As such, a third-party system could analyze and work with the received data while preserving the privacy of the raw data [190, 191]. **Federated learning for autonomous vehicle privacy protection:** Federated learning is a distributed machine learning technique that allows models to be trained collaboratively without directly sharing the data [192]. Instead of transmitting individual client data to a central server, the central server sends its model to the clients, and each client trains the model with its own data. This approach helps protect the privacy of the data collected by autonomous vehicles while still allowing for model improvement [187, 193]. **Personalized \(k\) anonymity:** Personalized \(k\) anonymity is a privacy preservation technique that ensures that query contents submitted by users in autonomous vehicles are sufficiently protected. It achieves this by perturbing location information or applyinganonymity techniques, which group queries together to provide privacy while maintaining query utility [194].