# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 3 Background Concepts
### Anonymization Tools  
Various off-the-shelf privacy-model-based data de-identification tools have been made available in the past. These tools are commonly adopted for de-identifying tab ular data, and a few for unstructured data, in different contexts such as healthcare, financial and governmental. There are plenty of open-source options. ARX [72] is considered one of the main anonymization tools [73], as it supports a wide variety of (i) privacy and risk models, such \(k\)-anonymity [50], \(l\)-diversity, \(t\)-proximity, \(\delta\)-presence and differential privacy; (ii) transforming methods for data, such as local and global transformation schemes, value generalization, random sampling, record deletion, attribute and cells, micro-aggregation, top and bottom coding and categorization; risk analyzing of re-identification methods and the usefulness of the resulting data, providing general purpose models, which are cell oriented, with records and attributes [74]. For this reason, in the literature [75] it is used in several studies, either for the anonymization of datasets [76; 77; 78; 79; 80] or for the analysis of re-identification risks [81]. Amnesia [82] is another popular anonymization tool [73], following the General Data Protection Regulation (GDPR) guidelines and supporting a few privacy models, such as \(k\)-anonymity and \(k^{m}\)-anonymity [83; 84]. \(\mu\)-ARGUS [85] is software that aims to help the production of secure microdata. The name ARGUS is an acronym for AntiRe-Identification General Utility System. Initially designed as a private tool, the latest releases have transitioned to open source. To make the microdata safe, the \(k\)-anonymity privacy model is used in most steps but it is also possible to apply additional transformations, such as local suppression, category grouping, noise addition and synthetic data [86]. Like \(\mu\)-ARGUS, sdMicro [87] is an R package that allows anonymization of microdata. SDC is an abbreviation for Statistical Disclosure Control. sdMicro was developed to assist research on the generation of microdata for public use. In sdMicro, two privacy models are used, \(k\)-anonymity and \(l\)-diversity, as well as methods for data transformation, such as randomization, top and bottom coding, suppression and recoding [18]. Anonimatron [88] performs data anonymization through pseudonymization, and allows fake Roman names, email addresses and universal unique identifiers to be generated, and claims to be GDPR compliant [18]. CHORUS [89] is a framework that provides a Scala library that allows the implementation of differential privacy methods in a cooperative model. The g9 Anonymizer [90] is a tool that comes as an Eclipse plugin, which provides programmable anonymization logic. To achieve data de-identification, the plugin supports data transformations, such as masking, scrambling, data generation synthetics and suppression. The University of Texas at Dallas Anonymization Toolbox [91] is software developed at the UT Dallas Data Security and Privacy Lab and implements six anonymization methods: Datafly [92], Mondrian Multidimensional \(k\)-Anonymity [93], Incognito [94], Incognito with \(l\)-diversity [95], Incognito with \(t\)-closeness [64] and Anatomy [96]. The Cornell Anonymization Toolkit (CAT) [97] is another free tool that allows data anonymization with an intuitive interface. The tool supports the Incognito algorithm, and the \(l\)-diversity and \(t\)-proximity privacy models [98]. Some other tools are also free for use but their source code is not available. The Tool for Interactive Analysis of Microdata Anonymization Techniques (TIAMAT) [99] supports different anonymization algorithms, such as Mondrian [93] and k-Member [100] as well as multiple models for analyzing and optimizing the utility of output data, as well as \(k\)-anonymity, \(l\)-diversity and \(t\)-proximity privacy models.