Patterns of Data Anonymization

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

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

Ata de Conferência Internacional

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

Páginas: 1-9

Indexação

Scopus - 1 Citação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica