# Privacy and Security in Cognitive Cities: A Systematic Review
### Privacy and Security in the Cognitive City  
Given that citizens' data are the primary material that allows the cognitive city decisions and that these decisions influence daily lives of citizens, protecting this information becomes crucial, not only at the individual level, but also at the societal level. First of all, the ubiquitous collection of citizen-related information at the city level introduces serious threats on individual privacy, such as: personal information disclosure, appropriation, or stigmatization. Thus, data will need to be anonymized. Even in aggregated data sets, there still exists a risk of re-identification of the individuals. As a consequence, statistical disclosure control methods should be applied prior to releasing any information [8; 9]. Furthermore, the availability of diverse urban-wide data sources (e.g., transportation, healthcare, energy, surveillance cameras, etc.) plus the inferential capabilities of cognitive and AI systems, deepens the knowledge on citizens' daily activities. This allows for a richer and more detailed profiling and modeling on citizens' actions: not only we could answer questions such as: _'who is he with? ', 'where is she? ', 'where does he use to buy groceries? '_, etc., but also _'what will he likely want to do tomorrow at six?'_. Despite potential benefits, individuals could suffer an invasion of privacy, and a reduction of liberty. Also, from a social perspective, the consequences could be even more dramatic, opening the door to threats, such as over-surveillance, power imbalance, and manipulation in favour of governments, corporations, or whoever is in control of the city data. Figure 1: A cognitive city scenario. Given that cognitive cities will become the natural environment for the interaction among humans and cognitive systems (e.g., autonomous cars), failures or malfunctions in this context can paralyze the city activities, causing a deep impact on citizens' lives. When these malfunctions occur on mission-critical infrastructures, such as healthcare, damages can be vast in terms of trust and economic loss and, even further, they can also cause harm to individuals and lost of human lives. Moreover, far from simply protecting the city from occasional breakdowns and malfunctions, cognitive city infrastructures will need to be prepared to face denial-of-service, ransomware, and other disruptive attacks, and implement proper security protection countermeasures. Also, these autonomous decision-making processes rely on true, accurate, and complete data to make proper rational decisions, but important issues can arise if the data gathered are incomplete, inaccurate, or wrong. Furthermore, the impact of data quality and integrity-related issues can become paramount if data are maliciously injected, modified, or deleted by malicious agents. Particularly, machine learning (ML) models are prone to data poisoning attacks, where a group of data points are stealthily added to the training set so that the algorithm output (i.e., tag) satisfies the attacker intentions. These data-set-oriented attacks can be carried out through sophisticated high-end methods [10], or by simpler trivial ones, like the artist that recently tricked Google Maps injecting false traffic jam, by simply dragging a cart with ninety nine smartphones while connected to the _ask for directions_ feature [11]. Recalling the aforementioned traffic lights example, the consequences of altering the information workflow amongst the involved agents can be fatal. With the aim to highlight the importance of security and privacy aspects in this context, next, we list some privacy and information security-related situations within cognitive cities, and their possible consequences. Illustrative examples of privacy and information security-related situations in a cognitive city:  
* Following the news of a cognitive city app being hacked, the citizens become reluctant to participate in the cognitive city project that the government is developing.