## 7 Risk Analysis
### Privacy Measures  
The evolution of automobiles has led to the collection of an increasingly amount of information about every aspect of the driving experience, prompting manufacturers of such technologies to deal with a large amount of sensitive information. This, in turn, makes privacy a fundamental aspect of the process of creating new technologies of this kind. Considering the privacy flaws highlighted in the previous subsection, some countermeasures must be taken into consideration when developing a privacy-by-design system. Constant vehicle geolocation captures every detail of users' daily routines and movements, making it possible for third parties to exploit this information. Moreover, data collection and processing can unveil extra personal and sensitive information such as contacts stored or, if using the ACN system, even images captured inside the car. This underscores the critical need to implement privacy countermeasures to safeguard this information. **Privacy by default** is increasing the proactivity at the beginning of the developing stage, considering privacy not anymore as a passive activity, to be taken care of when privacy issues are detected, but before they happen. Consideration of privacy at an earlier stage is able to enhance **transparency**, so there will be open and clear communication about data collection. The driver must be informed about the collected data and must be able to stop the collection whenever they believe it is not necessary anymore. Moreover, data produced by vehicle systems are usually used by multiple parties, **data minimization** can be put in place so that the collected data will limited to what is necessary for specific functionalities. Another interesting property to be considered, especially for the architecture defined by [PERSON] et al. [27] is **data anonymization**; this is particularly relevant for ML models where personal data are used to feed models. Among other properties, it is necessary to recall **user consent**, so the permission will be explicit and informed, **purpose binding**, so the data will be only used for the purposes explicitly communicated to the user during consent, **data portability**, so the user has the right to access and retrieve their personal data, and **the right to be forgotten**, so the user's entitled to have their personal data erased. General Data Protection Regulation (GDPR) in Europe defines the way in which these methods are applied. To assess the risks and prevent threats in a proactive way, a model like LINDDUN can be considered:  
* **Linkability**: Apparently unrelated data can be linked together to identify a person or reveal sensitive information. * **Identifiability**: An individual can be identified through a collection of data. * **Non-repudiation**: The ability of an individual to deny an action or event they have undertaken. * **Detectability**: Detecting that a user's personal data has been collected and used without their consent. * **Disclosure of information**: Collected data are disclosed to unauthorized third parties. * **Unawareness**: The user is unaware that specific data are being collected. * **Non-compliance**: The organization does not conform to privacy policies and laws. Numerous privacy preservation strategies can be implemented to lower risks at various levels in light of the mentioned designs and related threats: 1. **Differential privacy**: Ensures that the addition or removal of a single individual's data does not significantly impact the results of a data analysis. It allows for the collection of aggregate information while minimizing the risk of identifying individual contributors. 2. **Synthetic data**: Generating artificial data that retains statistical properties of the original dataset, but does not contain actual sensitive information. This helps in sharing information for analysis while maintaining privacy. 3. **Homomorphic encryption**: An encryption that allows computations to be performed on encrypted data without decrypting it first, preserving privacy during data processing. 4.