# Privacy and Security in Cognitive Cities: A Systematic Review
###### Abstract  
The emerging paradigm of the _cognitive city_, which augments smart cities with learning and behavioral change capabilities, is gaining increasing attention as a promising solution to the challenges of future mega-cities. Cognitive cities are built upon artificial learning and behavioral analysis techniques founded on the exploitation of human-machine collective intelligence. Hence, cognitive cities rely on the sharing of citizens' daily-life data, which might be considered sensitive personal data. In this context, privacy and security of the shared information become critical issues that have to be addressed to guarantee the proper deployment of cognitive cities and the fundamental rights of people. This article provides a thorough literature review using the recommendations for systematic reviews proposed by [PERSON] et al. and the PRISMA statement. We analyze peer-reviewed publications indexed in ACM Digital Library, IEEE Xplore, Scopus, and Web of Science until July 2020. We identify the main challenges on privacy and information security within cognitive cities, and the proposals described in the literature to address them. We conclude that many challenges remain open and we suggest several research lines that will require further examination in the years to come. cognition city; security; privacy; artificial intelligence; cybersecurity 202120211 May 20214 May 202  
connected learning theories (i.e., _Connectivism_[3]) have allowed the development of the new augmented urban paradigm of _cognitive cities_, which are drawing the attention of the research community. Cognitive cities learn and adapt their behavior based on past experiences and are able to sense, understand, and respond to changes in their environment [4]. There is a variety of views on the concept in the literature [5; 6] but, to sum up, a cognitive city exploits the collective intelligence of the city, that is, the pervasive bidirectional flows of information that circulate among humans and the city. These information flows create what is known as an _intelligence amplification loop_[7], which allows the city to _learn_ from the constant interaction between agents (humans and machines) and _adapt_ as the environment changes, achieving sustainability and resiliency. Unlike smart cities, cognitive cities use other sources of information, besides technological sources, to _sense_ their conditions: thanks to the interactions among human and non-human agents, cognitive cities are able to use the cultural, behavioral, spatial, and political information that makes up the _personality_ of a city. Let us illustrate the concept of cognitive city with a very simple example that compares its behavior to that of a smart city and a regular city:  
* In a _regular_ city, traffic lights are hardwired, and their behavior is fixed: if a change is needed, controls have to be rewired. * In a _smart_ city, traffic lights react to the data coming from nearby sensors to regulate the flow of traffic, and to provide an efficient response when needed, such as in case of traffic incidents. * In a _cognitive_ city, traffic lights learn from humans and vehicles passing by, generate hypothesis about the future, and evaluate the possible consequences of their decisions. Hence, they are no longer reactive, but proactive. Following this traffic lights example, let us enrich it so as to further highlight the features of cognitive cities. Figure 1 illustrates the scenario that is next explained:  
An intelligent vehicle has a blowout, and remains stopped blocking a lane. Due to the incident, the communication capabilities of the intelligent car have become affected and it is not able to notify its state to nearby agents, thus remaining unresponsive and blocking the lane 1. A nearby citizen, who has witnessed the incident, reports it by sending an alert using his/her smartphone. This alert is, therefore, forwarded to other nearby agents such as other citizens, vehicles and smart things 2.