Survey on Privacy-Preserving Techniques for Microdata Publication

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Carvalho, T (Autor) Outra; Moniz, N (Autor) Outra; Faria, P (Autor) Outra; antunes, l (Autor) FCUP

Revista

Título: ACM Computing Surveys

Vol. 55

Páginas: 1-42

ISSN: 0360-0300

Editora: ACM

Indexação

ISI Web of Knowledge - 14 Citações

Scopus - 16 Citações

Outras Informações

ID Authenticus: P-00W-30N

DOI: 10.1145/3588765

Abstract (EN): The exponential growth of collected, processed, and shared microdata has given rise to concerns about individuals' privacy. As a result, laws and regulations have emerged to control what organisations do with microdata and how they protect it. Statistical Disclosure Control seeks to reduce the risk of confidential information disclosure by de-identifying them. Such de-identification is guaranteed through privacy-preserving techniques (PPTs). However, de-identified data usually results in loss of information, with a possible impact on data analysis precision and model predictive performance. The main goal is to protect the individual's privacy while maintaining the interpretability of the data (i.e., its usefulness). Statistical Disclosure Control is an area that is expanding and needs to be explored since there is still no solution that guarantees optimal privacy and utility. This survey focuses on all steps of the de-identification process. We present existing PPTs used in microdata de-identification, privacy measures suitable for several disclosure types, and information loss and predictive performance measures. In this survey, we discuss the main challenges raised by privacy constraints, describe the main approaches to handle these obstacles, review the taxonomies of PPTs, provide a theoretical analysis of existing comparative studies, and raise multiple open issues.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 42