Privacy-Preserving Techniques in Social Distancing Applications: A Comprehensive Survey

  • Arwa Alrawais
  • , Fatemah Alharbi
  • , Moteeb Almoteri
  • , Beshayr Altamimi
  • , Hessa Alnafisah
  • , Nourah Aljumeiah

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

During the world’s challenge to confront the rapidly spreading coronavirus disease (COVID-19) pandemic and the consequent heavy losses and disruption to society, returning to normal life has become a demand. Social distancing, also known as physical distancing, plays a pivotal role in this scenario. Social distancing is a practice to maintain a safe space between a person and others who are not from the same household, preventing the spread of contagious viral diseases. To support this case, several public authorities and governments around the world have proposed social distancing applications (also known as contact-tracing apps). However, the adoption of these applications is arguable because of concerns regarding privacy and user data protection. In this study, we present a comprehensive survey of privacy-preserving techniques for social distancing applications. We provide an extensive background on social distancing applications, including measuring the physical distance between people. We also discuss various privacy-preserving techniques that are used by social distancing applications; specifically, we thoroughly analyze and compare these applications, considering multiple features. Finally, we provide insights and recommendations for designing social distancing applications while reducing the burden of privacy problems.

Original languageEnglish
Pages (from-to)325-341
Number of pages17
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume26
Issue number3
DOIs
StatePublished - May 2022

Keywords

  • coronavirus disease
  • COVID-19
  • location-based service (LBS)
  • privacy-preserving
  • social distancing

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