# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 7 Current Limitations  
Anonymization is the process of eliminating any information that relates to an identified or identifiable person to prevent re-identification by any reasonable means. However,in practice, many anonymization techniques that are currently in use are ineffective, leaving personal information vulnerable to re-identification; while pseudonymization allows for the reconstruction of the original dataset by design, anonymization should make re-identification impossible.  
The ISO/IEC 20889:2018 [66] standard provides guidelines for privacy-enhancing data de-identification techniques, including the use of HE as a cryptographic tool for de-identification. HE has several benefits, such as protecting sensitive details while allowing data to be analyzed and processed. However, traditional encryption still contains personal data and may not be reliable or secure. Additionally, HE requires significant computational resources and is currently slow [184], making it less practical for some use cases, and requires accelerators, such as Amazon AWS FPGA. There is also a lot of hard work required to get multiparty computation up and running quickly and with all the security properties correctly implemented, in order to guarantee the privacy of inputs and the correct outputs. Furthermore, multiparty computation still needs to be based on an authenticated protocol so that there are no impersonation attacks, which can also limit its use.  
A recent study by [185] evaluated the potential privacy risks of using federated learning in smart cities and found that, even with \(k\)-anonymity, the technique could still leak private information and allow for re-identification. Similarly, a study by [121] found that it was possible to re-identify up to 95% of individuals in a dataset of anonymous call detail records (CDRs) used for traffic analysis in a smart city, despite \(k\)-anonymity being applied.  
The current methods of anonymization and pseudonymization are inadequate to safeguard personal data in smart city systems. Advanced privacy-preserving techniques are needed for proper protection of individual privacy. Pseudonymization, although cheap and efficient, may not always be enough, and even de-characterized data can still lead to re-identification and be subject to GDPR regulations. Anonymization is the most effective approach for privacy compliance but complete anonymity is not possible, especially with visual data. The use of AI-based tools can speed up the process but 100% protection cannot be guaranteed.  
Another key limitation is scalability. Anonymization and pseudonymization techniques may not be able to handle large and complex datasets effectively, particularly when multiple data sources are involved. This can result in data loss and reduced quality of the anonymized data, making it less useful for analysis and decision-making. Another limitation is the potential loss of information during the anonymization process. Therefore, balancing privacy and utility is another major challenge in the field of anonymization and pseudonymization; while it is important to protect personal information, the anonymized data must also retain sufficient information to be useful for analysis and decision-making. This balance is difficult to achieve, particularly in the context of smart cities, where a large amount of data is collected from various sources.