## 4 Cyber Security Attacks in Vehicular Ad Hoc Networks (VANETs)
### Threats to Confidentiality in Emotionally Intelligent Autonomous Vehicles  
Confidentiality attacks, although not perceived as the most significant threats to vehicular system security, nevertheless constitute considerable privacy risks. Preserving the confidentiality of data transmitted within vehicular networks, such as the geographical location of autonomous vehicles, intelligent transport systems (ITS) safety alerts, and driver information, is paramount. Possible data breaches can be deterred through secure transmission techniques and fortified data encryption protocols.  
An illustrative confidentiality threat in the realm of vehicular networks is the eavesdropping attack. During standard vehicle operation, vehicles frequently broadcast beacon signals encapsulating a wealth of data, such as the vehicle's identity, current location, velocity, and acceleration. This disseminated information is vulnerable to unauthorized interception, potentially breaching the privacy of the system. An adversary armed with the appropriate tools could execute an eavesdropping attack, harvesting precious data from the transmitted information. By associating this extracted location data over time, the adversary could track the vehicle's path and possibly manipulate it for malicious activities.  
Given its passive nature, eavesdropping presents a formidable challenge, as its detection is difficult, especially in a broadcast wireless communication context. Nonetheless, the successful orchestration of eavesdropping can be thwarted by employing data encryption protocols to shield data privacy or anonymization techniques to protect identity and location data. Anonymity is generally realized using group signatures [66] or temporary certificates, also known as pseudonyms [67].  
In the context of emotionally intelligent autonomous vehicles, confidentiality attacks bear an additional layer of intricacy and risk. \"Emotionally intelligent autonomous vehicles\" refer to autonomous vehicles that are equipped with the ability to perceive, comprehend, and appropriately react to human emotions. These vehicles utilize a range of technologies, such as AI, ML, and sensor technologies, complemented by effective computing capabilities. They can identify and respond to emotional cues by recognizing facial expressions, interpreting voice sentiments, processing physiological signals, and more. Their goal is to enhance the passengers' comfort, safety, and overall experience by adapting to their emotional states.  
The emotional data in these vehicles represent a core component of their intelligent system and are highly personal and sensitive. Thus, the implications of an eavesdropping attack could extend beyond mere vehicle tracking, potentially leading to unauthorized access and exploitation of a passenger's emotional data. To effectively safeguard this sensitive information, advanced encryption, and anonymization techniques must be amalgamated within the system's data communication protocol. This integration should ensure that even in the event of an eavesdropping attack, the intercepted data would not be useful to the adversary.  
Moreover, emotionally intelligent autonomous vehicles may necessitate additional privacy protection layers due to the nature of the processed data. For instance, homomorphic encryption could be employed to allow the system to analyze and react to emotional data without ever needing to decrypt them, thereby maintaining privacy. Additionally, differential privacy concepts could be applied to ensure that the system's responses do not inadvertently disclose sensitive information about the passengers' emotional states. These advanced methods could form a vital part of a comprehensive strategy for preserving data confidentiality in emotionally intelligent autonomous vehicles.