Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

Paulo Silva,Edmundo Monteiro,Paulo Simoes

doi:10.1109/access.2021.3049599

Paulo Silva, Edmundo Monteiro + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3049599

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Abstract

Private data is transmitted and stored online every second. Therefore, security and privacy assurances should be provided at all times. However, that is not always the case. Private information is often unwillingly collected, sold, or exposed, depriving data owners of their rightful privacy. In this article, various privacy threats, concepts, regulations, and personal data types are analyzed. An overview of Privacy Enhancing Technologies (PETs) and a survey of anonymization mechanisms, privacy tools, models, and metrics are presented together with an analysis of respective characteristics and capabilities. Moreover, this article analyses the applicability of the reviewed privacy mechanisms on today's Cloud Services and identifies the current research challenges to achieve higher privacy levels in the Cloud.

Highlights

Over time different technologies and solutions have been proposed to secure users’ information online or offline
Personal information is every piece of information that is related to an identifiable person
A data curator must be experienced with anonymization mechanisms and with the regulations available, which is why we review privacy regulations

Summary

INTRODUCTION

Over time different technologies and solutions have been proposed to secure users’ information online or offline. Tools, policies, and other mechanisms that provide privacy protection can be classified as PETs. This article stresses one of the ways of directly achieving information privacy: by performing data anonymization. This article stresses one of the ways of directly achieving information privacy: by performing data anonymization These mechanisms may help to avoid (or to minimize) the problems mentioned above, but they increase safety while publishing data (e.g., preserving privacy on publicly released data). While this survey presents an overview of the different types of PETs, it emphasizes data anonymization mechanisms, the related privacy metrics, and their applicability in Cloud contexts.

BACKGROUND

PRIVACY METRICS

PRIVACY TOOLS

OPEN ISSUES AND RESEARCH CHALLENGES

Findings

CONCLUSION AND FINAL REMARKS