JACIII Vol.26 No.3 pp. 325-341
doi: 10.20965/jaciii.2022.p0325


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

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

*College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University
Al-Kharj, Saudi Arabia

**College of Computer Science and Engineering, Taibah University
Yanbu, Saudi Arabia

***College of Business Administration, King Saud University
Riyadh, Saudi Arabia

January 9, 2022
February 14, 2022
May 20, 2022
coronavirus disease, COVID-19, privacy-preserving, social distancing, location-based service (LBS)

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.

Cite this article as:
A. Alrawais, F. Alharbi, M. Almoteri, B. Altamimi, H. Alnafisah, and N. Aljumeiah, “Privacy-Preserving Techniques in Social Distancing Applications: A Comprehensive Survey,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.3, pp. 325-341, 2022.
Data files:
  1. [1] F. N. Wirth, M. Johns, T. Meurers, and F. Prasser, “Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review,” JMIR Mhealth Uhealth, Vol.8, No.11, e22594, 2020.
  2. [2] L. Thunström, S. C. Newbold, D. Finnoff, M. Ashworth, and J. F. Shogren, “The benefits and costs of using social distancing to flatten the curve for COVID-19,” J. of Benefit-Cost Analysis, Vol.11, No.2, pp. 179-195, 2020.
  3. [3] T. VoPham, M. D. Weaver, J. E. Hart, M. Ton, E. White, and P. A. Newcomb, “Effect of social distancing on COVID-19 incidence and mortality in the US,” MedRxiv, 2020.
  4. [4] C. Fraser, S. Riley, R. M. Anderson, and N. M. Ferguson, “Factors that make an infectious disease outbreak controllable,” Proc. of the National Academy of Sciences, Vol.101, No.16, pp. 6146-6151, 2004.
  5. [5] V. J. Lee, C. J. Chiew, and W. X. Khong, “Interrupting transmission of COVID-19: lessons from containment efforts in Singapore,” J. of Travel Medicine, Vol.27, No.3, taaa039, 2020.
  6. [6] M. Greenstone and V. Nigam, “Does social distancing matter?,” University of Chicago, Becker Friedman Institute for Economics Working Paper, No.2020-26, 2020.
  7. [7] G. Avitabile, V. Botta, V. Iovino, and I. Visconti, “Towards defeating mass surveillance and SARS-CoV-2: The Pronto-C2 fully decentralized automatic contact tracing system,” Proc. of the Workshop on Secure IT Technologies Against COVID-19 (CoronaDef), 2020.
  8. [8] R. Thomas, Z. A. Michaleff, H. Greenwood, E. Abukmail, and P. Glasziou, “Concerns and misconceptions about the Australian government’s COVIDsafe app: cross-sectional survey study,” JMIR Public Health and Surveillance, Vol.6, No.4, pp. e23081, 2020.
  9. [9] D. J. Solove, “Understanding privacy,” Harvard University Press, 2008.
  10. [10] H. Cho, D. Ippolito, and Y. W. Yu, “Contact tracing mobile apps for COVID-19: Privacy considerations and related trade-offs,” arXiv preprint, arXiv:2003.11511, 2020.
  11. [11] J. M. Heffernan, R. J. Smith, and L. M. Wahl, “Perspectives on the basic reproductive ratio,” J. of the Royal Society Interface, Vol.2, No.4, pp. 281-293, 2005.
  12. [12] A. Sedik, A. M. Iliyasu, B. A. El-Rahiem et al., “Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections,” Viruses, Vol.12, No.7, p. 769, 2020.
  13. [13] C. T. Nguyen, Y. M. Saputra, N. van Huynh et al., “A comprehensive survey of enabling and emerging technologies for social distancing – part II: Emerging technologies and open issues,” IEEE Access, Vol.8, pp. 154209-154236, 2020.
  14. [14] R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, “Artificial intelligence (AI) applications for COVID-19 pandemic,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, Vol.14, No.4, pp. 337-339, 2020.
  15. [15] A. B. Dar, A. H. Lone, S. Zahoor, A. A. Khan, and R. Naaz, “Applicability of mobile contact tracing in fighting pandemic (COVID-19): Issues, challenges and solutions,” Computer Science Review, 100307, 2020.
  16. [16] M. Ndiaye, S. S. Oyewobi, A. M. Abu-Mahfouz, G. P. Hancke, A. M. Kurien, and K. Djouani, “IoT in the wake of COVID-19: A survey on contributions, challenges and evolution,” IEEE Access, Vol.8, pp. 186821-186839, 2020.
  17. [17] B. Armbruster and M. L. Brandeau, “Contact tracing to control infectious disease: when enough is enough,” Health Care Management Science, Vol.10, No.4, pp. 341-355, 2007.
  18. [18] T. M. Yasaka, B. M. Lehrich, and R. Sahyouni “Peer-to-peer contact tracing: A privacy-preserving smartphone app,” JMIR mHealth and uHealth, Vol.8, No.4, e18936, 2020.
  19. [19] S. Vaudenay, “Centralized or decentralized? The contact tracing dilemma,” IACR Cryptol. ePrint Arch., p. 531, 2020.
  20. [20] J. Bay, J. Kek, A. Tan, C. S. Hau, L. Yongquan, J. Tan, and T. A. Quy, “BlueTrace: A privacy-preserving protocol for community-driven contact tracing across borders,” Government Technology Agency-Singapore, Tech. Rep., 2020.
  21. [21] C. Castelluccia, N. Bielova, A. Boutet, M. Cunche, C. Lauradoux, D. Le Métayer, and V. Roca, “Robert: ROBust and privacy-preserving proximity tracing,” HAL Open Science, hal-026112652020, 2020.
  22. [22] T. Altuwaiyan, M. Hadian, and X. Liang, “EPIC: efficient privacy-preserving contact tracing for infection detection,” 2018 IEEE Int. Conf. on Communications (ICC), pp. 1-6, 2018.
  23. [23] A. De Carli, M. Franco, A. Gassmann, C. Killer, B. Rodrigues, E. Scheid, D. Schoenbaechler, and B. Stiller, “WeTrace – A privacy-preserving mobile COVID-19 tracing approach and application,” arXiv preprint, arXiv:2004.08812, 2020.
  24. [24] C. Zhang, C. Xu, K. Sharif, and L. Zhu, “Privacy-preserving contact tracing in 5G-integrated and blockchain-based medical applications,” Computer Standards & Interfaces, Vol.77, 103520, 2021.
  25. [25] C. Troncoso, M. Payer, J.-P. Hubaux et al., “Decentralized privacy-preserving proximity tracing,” arXiv preprint, arXiv:2005.12273, 2020.
  26. [26] Y. Luo, C. Zhang, Y. Zhang, C. Zuo, D. Xuan, Z. Lin, A. C. Champion, and N. Shroff, “ACOUSTIC-TURF: Acoustic-based privacy-preserving COVID-19 contact tracing,” arXiv preprint, arXiv:2006.13362, 2020.
  27. [27] R. L. Rivest, J. Callas, R. Canetti et al., “The PACT protocol specification,” Private Automated Contact Tracing Team, Massachusetts Institute of Technology (MIT), MIT Tech. Rep. 0.1, 2020.
  28. [28] J. Chan, D. Foster, S. Gollakota et al., “PACT: Privacy-sensitive protocols and mechanisms for mobile contact tracing,” IEEE Data Engineering Bulletin, Vol.43, No.2, pp. 15-35, 2020.
  29. [29] N. Ahmed, R. A. Michelin, W. Xue, S. Ruj, R. Malaney, S. S. Kanhere, A. Seneviratne, W. Hu, H. Janicke, and S. K. Jha, “A survey of COVID-19 contact tracing apps,” IEEE Access, Vol.8, pp. 134577-134601, 2020.
  30. [30] C. Castelluccia, N. Bielova, A. Boutet, M. Cunche, C. Lauradoux, D. L. Métayer, and V. Roca, “Desire: A third way for a European exposure notification system leveraging the best of centralized and decentralized systems,” arXiv preprint, arXiv:2008.01621, 2020.
  31. [31] N. Trieu, K. Shehata, P. Saxena, R. Shokri, and D. Song, “Epione: Lightweight contact tracing with strong privacy,” Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, pp. 95-107, 2020.
  32. [32] P. Barsocchi, A. Calabrò, A. Crivello, S. Daoudagh, F. Furfari, M. Girolami, and E. Marchetti, “COVID-19 & privacy: Enhancing of indoor localization architectures towards effective social distancing,” Array, Vol.9, 100051, 2021.
  33. [33] B. Sookman, “AI and Contact Tracing: How to Protect Privacy While Fighting the COVID-19 Pandemic,” Macdonald-Laurier Institute Publication, 2020.
  34. [34] D. Yang, E. Yurtsever, V. Renganathan, K. A. Redmill, and Ü. Özgüner, “A vision-based social distancing and critical density detection system for COVID-19,” Sensors, Vol.21, No.13, 4608, 2020.
  35. [35] A. J. Sathyamoorthy, U. Patel, Y. A. Savle, M. Paul, and D. Manocha, “COVID-robot: Monitoring social distancing constraints in crowded scenarios,” arXiv preprint, arXiv:2008.06585, 2020.
  36. [36] P. V. Klaine, L. Zhang, B. Zhou, Y. Sun, H. Xu, and M. Imran, “Privacy-preserving contact tracing and public risk assessment using blockchain for COVID-19 pandemic,” IEEE Internet of Things Magazine, Vol.3, No.3, pp. 58-63, 2020.
  37. [37] A. Ksentini and B. Brik, “An edge-based social distancing detection service to mitigate COVID-19 propagation,” IEEE Internet of Things Magazine, Vol.3, No.3, pp. 35-39, 2020.
  38. [38] M. Gupta, M. Abdelsalam, and S. Mittal, “Enabling and enforcing social distancing measures using smart city and its infrastructures: a COVID-19 use case,” arXiv preprint, arXiv:2004.09246, 2020.
  39. [39] M. S. Alrahhal, M. U. Ashraf, A. Abesen, and S. Arif, “AES-route server model for location based services in road networks,” Int. J. of Advanced Computer Science and Applications, Vol.8, No.8, pp. 361-368, 2017.
  40. [40] N. Guo, L. Ma, and T. Gao, “Independent mix zone for location privacy in vehicular networks,” IEEE Access, Vol.6, p. 16842-16850, 2018.
  41. [41] C. Kalaiarasy, N. Sreenath, and A. Amuthan, “Location privacy preservation in VANET using mix zones – A survey,” Proc. of the 2019 Int. Conf. on Computer Communication and Informatics (ICCCI), pp. 1-5, 2019.
  42. [42] A. M. Basahel, A. A. A. Sen, M. Yamin, and S. Alqahtani, “Bartering method for improving privacy of LBS,” Int. J. of Computer Science and Network Security (IJCSNS), Vol.19, No.2, pp. 207-213, 2019.
  43. [43] M. Yamin and A. A. A. Sen, “A new method with swapping of peers and fogs to protect user privacy in IoT applications,” IEEE Access, Vol.8, pp. 210206-210224, 2020.
  44. [44] P. Jagwani and S. Kaushik, “Secure cloaking area based on user profile similarity,” Int. J. Eng. Technol., Vol.8, No.6, pp. 458-461, 2016.
  45. [45] P. Kalnis, G. Ghinita, K. Mouratidis, and D. Papadias, “Preventing location-based identity inference in anonymous spatial queries,” IEEE Trans. on Knowledge and Data Engineering, Vol.19, No.12, pp. 1719-1733, 2007.
  46. [46] A. A. A. Sen, A. Alnsour, S. A. Aljwair, S. S. Aljwair, H. I. Alnafisah, and B. A. Altamimi, “Fog mix-zone approach for preserving privacy in IoT,” 2021 8th Int. Conf. on Computing for Sustainable Global Development (INDIACom), pp. 405-408, 2021.
  47. [47] A. A. A. Sen and M. Yamin, “Advantages of using fog in IoT applications,” Int. J. of Information Technology, pp. 1-9, 2020.

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