# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 3 Background Concepts
### Anonymization Tools
The System for Evaluating and Comparing RElational and Transaction Anonymization algorithms (SECRETA) [101] is focused on analyzing the effectiveness and efficiency of anonymization algorithms for tabular as well as set-valued data. SECRETA supports nine algorithms, four to deal with datasets with relational attributes (Incognito [94], Cluster [102], Top-down [103] and Full subtree bottom-up) and five to handle datasets with transaction attributes (COAT [104], PCTA [105], Apriori, LRA and VPA [106]). NLM-Scrubber [107] was developed by the National Library of Medicine to de-identify clinical texts. The goal of NLM-Scrubber is to generate adequate health information with the Health Insurance Portability and Accountability Act (HIPPA). Unlike other tools, NLM-Scrubber performs the de-identification of texts, replacing terms that represent information such as age, address, data and personally identifiable information (PII) by a tag that identifies only the type of information in the text. For example, in the text: \"Dr. [PERSON] visited the 98-year-old patient...\", the de-identified text would contain \"Dr. [PERSONALNAME] visited the [AGE90+] patient...\". From the private side, the need of data anonymization tools that protect individuals' and corporations' private activity in compliance with GDPR created a profitable market that explains the emergence of a myriad of tools. Aircloak Insights [108] is a private tool that acts as a proxy between the data analysts and the dataset. Aircloak Insights consists of two components: Insights Air and Insights Cloak. Insights Cloak is responsible for performing the analysis and anonymization of sensitive data, connecting to the databases that contain the sensitive data, without the need for changes. The anonymization is based on a combination of techniques used over time, such as \(k\)-anonymization, suppression, differential privacy noise, and top and bottom coding. As the limitations of traditional de-identification methods are becoming more evident, modern tools are developed to produce effective results with structured and unstructured data in a vast range of fields and sectors. The new tools mix traditional de-identification methods with new ones, such as synthetic data, natural language processing (NLP) and artificial intelligence. For example, CloverDX [109] is focused on de-identifying production-level datasets for development, visualization, testing, analytics or prototyping. The tool enable a set of data transformations based on a combination of masking and synthetic data generation. Similarly, BizDataX [110] focuses on enabling the anonymization of production data for developing and testing, and offers a data masking toolbox to conceal identities and sensitive data, achieving compliance with GDPR. Created to fit the needs of anonymization of a big pharmaceutical company, Gramener's Data Anonymization Solution [111] uses NLP to redact patients' private information from clinical trial documents, according to HIPAA and GDPR. Another useful tool for redacting sensitive documents is Docbyte's Real-time Automated Anonymization [112]. The tool uses artificial intelligence and machine learning in anonymizing data. The tool can black out or blur images and redact text considered sensitive using image-focused algorithms and object recognition. Most of the tools listed above have been designed to process only structured data, being unable to extract relevant information from unstructured natural language texts, geolocation data, video footage and images, which represent a significant and essential part of available data in smart cities. Nevertheless, in the following sections, we give some insights into what tools can be helpful in different contexts of the smart city.