A survey of privacy-preserving mechanisms for heterogeneous data types

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2021

Autores

Cunha, M (Autor) Outra; Mendes, R (Autor) Outra; João P. Vilela (Autor) FCUP

Revista

Título: Computer Science Review

Vol. 41

ISSN: 1574-0137

Editora: Elsevier

Indexação

ISI Web of Knowledge - 33 Citações

Classificação Científica

CORDIS: Ciências Físicas > Ciência de computadores

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-00V-5P2

DOI: 10.1016/j.cosrev.2021.100403

Abstract (EN): Due to the pervasiveness of always connected devices, large amounts of heterogeneous data are continuously being collected. Beyond the benefits that accrue for the users, there are private and sensitive information that is exposed. Therefore, Privacy-Preserving Mechanisms (PPMs) are crucial to protect users' privacy. In this paper, we perform a thorough study of the state of the art on the following topics: heterogeneous data types, PPMs, and tools for privacy protection. Building from the achieved knowledge, we propose a privacy taxonomy that establishes a relation between different types of data and suitable PPMs for the characteristics of those data types. Moreover, we perform a systematic analysis of solutions for privacy protection, by presenting and comparing privacy tools. From the performed analysis, we identify open challenges and future directions, namely, in the development of novel PPMs. (C) 2021 The Authors. Published by Elsevier Inc.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15