## 1 Introduction  
Internet of Things (IoT) has been largely employed around the world to provide a number of new services. In the IoT domain, things like everyday objects, places, and environments are interconnected with each other through the Internet [1]. This definition can serve as a basis for the design of Smart Cities. Smart cities are structures for real-time data collection and integration based on the use of sensors, applications, personal devices, and other interconnected resources [2]. When integrated into a computing platform, these features provide a set of smart services useful to solve urban problems. Such devices contribute to the sustainable development of cities and the improvement of the quality of life of their citizens. [PERSON] et al. [3] have identified four primary roles of the IoT in Smart Cities: (_i_) ensuring the ubiquitous connectivity between different objects; (_ii_) collecting a large amount of data that can be analyzed, stored, and shared; (_iii_) improving and facilitating the accessibility of services and enabling the creation of new intelligent and personalized services; and (_iv_) monitoring movements in different areas of the smart city, which can improve the level of security. Several infrastructures of a city can benefit from smart services. For instance, law enforcement organizations such as the police departments have been using license plate recognition (LPR) systems, also called license plate readers, to monitor and track vehicles circulating through the cities. As reported by the authors of [4], in 2002, police forces in England and Wales began to be equipped with LPR technology. United States (US) agencies followed suit around 2004, as did Canada and Australia. In less than one decade, LPR systems have been widely adopted by law enforcement agencies around the world as a force multiplier in law enforcement and an essential tool for fighting crime. LPR systems take photographs of vehicles and, through image recognition technologies, collect and store the license plate number, date, time, and location of each reading. Law enforcement organizations use these data to detect stolen vehicles, identify, and monitor suspect vehicles, or even wanted individuals. Concern has raised from the use of LPR systems based on the privacy of the captured data [5]. LPR systems do not distinguish between vehicles under criminal investigation and those that are not. This feature violates the data protection laws in countries that protect the storage of personal data without consent or motivation provided by law. Organizations that use LPR systems have faced questions from citizens and organizations that defend the privacy of individuals. The captured image of a vehicle, its license plate, location, and the date and time the image was recorded provide a basis for inferring personal characteristics about the driver. Therefore, it is clear the need for solutions to protect privacy in cities that use LPR systems for vehicle monitoring. These systems must be implemented considering guarantees of privacy of the data they capture, not allowing that data to be stored and processed in disagreement with the provisions of the current data protection laws. Based on a systematic literature review, we have identified a lack of solutions to guarantee privacy in LPR systems. Given this gap, in [6], we presented a proposal of a storage architecture that uses blockchain technology to guarantee the privacy of data captured by LPR systems. This architecture relies on the Ethereum platform, a decentralized network capable of executing smart contracts. In our model, each smart contract will match the privacy preferences of a license plate that will be anonymized through public encryption. Thus, the storage of data captured by the LPR system cannot be accomplished if privacy protection is enabled in the smart contract associated with the license plate. Our architecture uses a gateway to control access to the blockchain smart contracts.