# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques
#### 5.2.5 \(K\)-Anonymity  
Reference [165] presents a privacy notion of client-based personalized \(k\)-anonymity for autonomous vehicles querying services in cyber-physical systems, allowing users to specify different report sizes representing the anonymity level of each query content.  
For example, in a smart city that uses cameras to monitor traffic flow, the cameras may collect images of cars and license plate numbers. To protect the privacy of drivers, the license plate numbers can be replaced with a code that represents a cluster of at least \(k\) vehicles. This way, it would be impossible to identify a specific vehicle or driver from the data.  
The authors of [166] present an approach called heatmap confusion (HMC), which is a location privacy protection mechanism (LPPM) that acts to protect against re-identification attacks. It uses a heat map alteration process to confuse the attacker and to make the re-identification fall to the wrong user. Another approach also focuses on local mobility features: micro-mobility (e.g., individual geographical coordinates). LPPMs are often classified depending on the privacy guarantees they offer to users, mainly \(k\)-anonymity and differential privacy.  
\(L\)-diversity and \(l\)-proximity are enhancements for \(k\)-anonymity, which is a technique used to protect the privacy of individuals in a dataset by ensuring that each group of records (or quasi-identifiers) with the same values for certain attributes (e.g., age, gender) contains at least \(k\) records. \(L\)-diversity is a technique used to ensure that, within each group of records, there is a sufficient number of distinct sensitive attribute values (e.g., disease diagnosis). \(T\)-proximity is a technique used to ensure that, within each group of records, the sensitive attribute values are similar to one another.  
Reference [167] proposes a novel privacy-preserving data collection scheme for IoT-based healthcare service systems that utilizes clustering-based anonymity models to ensure privacy and prevent privacy attacks. The scheme aims to tackle various privacy threats, such as attribute and identity disclosure, and is efficient in reducing communication costs while improving privacy protection. The authors argue that this scheme proved to be more efficient in terms of information loss and data utility compared to \(k\)-anonymity. Another approach of the same authors, in another publication [168], proposes an attribute-focused privacy-preserving data publishing scheme for sharing healthcare data while protecting patient privacy. The scheme consists of a fixed-interval approach for numerical attributes and an improved \(l\)-diverse slicing approach for categorical and sensitive attributes. Experiments show improved accuracy of 13% in classification models and reduced information loss by 12% compared to similar approaches.  
In the context of smart cities, these techniques could be used to protect the privacy of individuals when sharing data about urban services, such as transportation or energy usage. For example, when sharing data about the usage of public transportation, \(l\)-diversity and \(t\)-proximity could be used to ensure that groups of individuals with similar travel patterns also have a sufficient number of distinct destinations and that the destinations are similar to one another.  
The TIAMAT and Cornell Anonymization Toolkit allow for creating \(k\)-anonymous, \(l\)-diverse and \(t\)-closeness-anonymous datasets. They are based on the concept of generalization and suppression to protect the privacy of individuals.  
[PERSON] et al. (2019) propose an improved version of \(k\)-degree-anonymization for preserving privacy in social network graphs, using the NeuroSVM hybrid technique which reduces distortion in average path length and information loss. The proposed technique is shown to have improved accuracy (over 75%) compared to existing methods in preserving privacy in social networks.