Patterns of Data Anonymization

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Monteiro M. (Author) Other; Filipe Figueiredo Correia (Author) FEUP; Queiroz P. (Author) Other; Ramos R. (Author) Other; Trigo D. (Author) Other; Gonçalves G. (Author) Other

Conference proceedings International

Title: Proceedings of the 29th European Conference on Pattern Languages of Programs, People, and Practices, EuroPLoP 2024, Irsee, Germany, July 3-7, 2024

Pages: 1-9

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-017-JMQ

DOI: 10.1145/3698322.3698337

Abstract (EN): Over the years, sensitive data has been growing in software systems. To comply with ethical and legal requirements, the General Data Protection Regulation (GDPR) recommends using pseudonymization and anonymization techniques to ensure appropriate protection and privacy of personal data. Many anonymization techniques have been described in the literature, such as generalization or suppression, but deciding which methods to use in different contexts is not a straightforward task. Furthermore, anonymization poses two major challenges: choosing adequate techniques for a given context and achieving an optimal level of privacy while maintaining the utility of the data for the context within which it is meant to be used. To address these challenges, this paper describes four new design patterns: Generalization, Hierarchical Generalization, Suppress Outliers, and Relocate Outliers, building on existing literature to offer solutions for common anonymization challenges, including avoiding linkage attacks and managing the privacy-utility trade-off.

Language: English

Type (Professor's evaluation): Scientific