A survey of privacy-preserving mechanisms for heterogeneous data types

• Type: Another Publication in an International Scientific Journal
• Date: 2021

Authors

Cunha, M (Author) Other; Mendes, R (Author) Other; João P. Vilela (Author) FCUP

Journal

Title: Computer Science Review

Vol. 41

ISSN: 1574-0137

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 39 Citations

Scientific classification

CORDIS: Physical sciences > Computer science

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15