Analysis of Data’s Privacy and Anonymity Aspects of Contact Tracing Apps via Smartphones – A Use Case of COVID-19
Keywords:ArogyaSetu, ARIMA model, Contact Tracing, Machine Learning, RFID tags, Smartphone Apps, time series prediction.
Privacy and anonymity aspects are playing a vital role in accessing smartphone apps. This is more evident in unexpected epidemic situations like COVID-19 while working with contact tracing apps. A human connectivity model is essential to analyse the widespread cases of viruses and vaccination patterns during the timeframe of March 2020 to May 2021. Smartphone apps that are supported by technologies like IoT and blockchain have already proven effective in tracing the Ebola epidemic. Thus, this technology, coupled with privacy-preserving features, would help to discover clusters with infectious contacts and alert the respective authorities. Besides, this can also allow us to understand the human connectivity model and the effectiveness of vaccines, which can aid in developing a plan of action for future epidemics. Hence, this article focuses on the analysis of data collected from contact tracing apps and a number of affected cases. It includes a study on early solutions with existing technologies, an overview and analysis of existing COVID-19 apps with vulnerabilities, proposed solutions, and data analysis on privacy and anonymity aspects of smartphone apps using the ARIMA model. It is evaluated by correlating it with the usage of contact tracing apps. The results assured a positive correlation between the number of downloads and the number of cases. This infers that even though the Indian government released these contact tracing apps, it all depends on the citizens to utilise them to their fullest. As a policy suggestion, it is stated that regardless of the prevalence of contact tracing apps, people must follow the rules and regulations suggested by the local health authorities and maintain social distancing in public places.
World Health Organization (WHO). “Weekly epidemiological update on COVID-19-23 March 2021 [Internet].” Geneva: WHO, 2021.
Dong Y, Yao Y. D., “IoT Platform for COVID-19 Prevention and Control: A Survey,” in IEEE Access, vol. 9, pp. 49929–49941, 2021, doi: 10.1109/ACCESS.2021.3068276.
Buchanan, William J., Muhammad Ali Imran, Masood Ur-Rehman, Lei Zhang, Qammer H. Abbasi, Christos Chrysoulas, David Haynes, Nikolaos Pitropakis, and Pavlos Papadopoulos. “Review and critical analysis of privacy-preserving infection tracing and contact tracing.” Frontiers in Communications and Networks 1 (2020): 583376.
Nagori, V. “AarogyaSetu”: The mobile application that monitors and mitigates the risks of COVID-19 pandemic spread in India. Journal of Information Technology Teaching Cases, 11(2), 66–80, 2021.
Mankar, Vikrant, M. Naravane, and Swarupa Chakole. “The rise and impact of Covid-19 in India: Aarogyasetu App.” Europ J Molec Clin Med 8.1, 2021.
Tellier, R., Li, Y., Cowling, B. J., and Tang, J. W., Recognition of aerosol transmission of infectious agents: a commentary. BMC infectious diseases, 19(1), 1–9, 2019.
Stedman, I. Colleen M. Flood, Vanessa MacDonnell, Jane Philpott, Sophie Thériault, and Sridhar Venkatapuram, eds. Vulnerable: The Law, Policy and Ethics of COVID-19. Ottawa, ON: University of Ottawa Press, 630 pp. Canadian Journal of Law and Society/La Revue Canadienne Droit et Société, 36(1), 185–187, 2021.
Ekong I, Chukwu E, Chukwu M, “COVID-19 Mobile Positioning Data Contact Tracing and Patient Privacy Regulations: Exploratory Search of Global Response Strategies and the Use of Digital Tools in Nigeria”, JMIR MhealthUhealth 2020;8(4):e19139. doi: 10.2196/19139.
Cecilia Panigutti, Michele Tizzoni, Paolo Bajardi, Zbigniew Smoreda, Vittoria Colizza. Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models. Royal Society Open Science, 2017, 4(5), pp. 160950. ⟨
World Health Organization, Coronavirus disease 2019 (COVID-19): situation report, 73, 2020.
Garg L., Chukwu E., Nasser N., Chakraborty C., G. Garg, “Anonymity Preserving IoT-Based COVID-19 and Other Infectious Disease Contact Tracing Model,” in IEEE Access, vol. 8, pp. 159402–159414, 2020, doi: 10.1109/ACCESS.2020.3020513.
S. M. Abu Adnan Abir, Shama Naz Islam, Adnan Anwar, Abdun Naser Mahmood, AmanMaung Than, “Building Resilience against COVID-19 Pandemic Using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress”, 2020, DOI: 10.3390/iot1020028.
Wang, L. L., Lo, K., Chandrasekhar, Y., Reas, R., Yang, J., Eide, D., … and Kohlmeier, S. Cord-19: The covid-19 open research dataset. ArXiv, 2020.
COVID, U., About Variants of the Virus that Causes COVID-19, 2021.
Ahmad, M., Riaz, Q., Zeeshan, M., “Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set”, Journal of Wireless Communication Network 2021, 10, 2021.
Azad, M. A., Arshad, J., Akmal, S. M. A., Riaz, F., Abdullah, S., Imran, M., and Ahmad, F., A first look at privacy analysis of COVID-19 contact-tracing mobile applications. IEEE Internet of Things Journal, 8(21), 15796–15806, 2020.
Leith, D. J., and Farrell, S. Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a commuter bus. Plos one, 16(4), e0250826, 2021.
Ahmad, Maged N Kamel Boulos, Ricardo Vinuesa, Junaid Qadir, “COVID-19 digital contact tracing applications and techniques: A review post initial deployments.” Transportation Engineering, vol. 5 (2021): 100072.
Elissa M. Redmiles,“User Concerns & Tradeoffs in Technology-facilitated COVID-19 Response”, Digital Government: Research and Practice, vol. 2, issue 1, pp. 1–12, 2021.
Kondylakis, H., Katehakis, D. G., Kouroubali, A., Logothetidis, F., Triantafyllidis, A., Kalamaras, I., … and Tzovaras, D, COVID-19 mobile apps: a systematic review of the literature. Journal of medical Internet research, 22(12), e23170, 2020.
Berthome, P., Fecherolle, T., Guilloteau, N., and Lalande, J. F., Repackaging android applications for auditing access to private data. In 2012 Seventh International Conference on Availability, Reliability and Security (pp. 388–396). IEEE, 2012.
Gupta, R., Bedi, M., Goyal, P., Wadhera, S., and Verma, V, Analysis of COVID-19 tracing tool in India: case study of AarogyaSetu mobile application. Digital Government: Research and Practice, 1(4), 1–8, 2020.
Aktay, A., Bavadekar, S., Cossoul, G., Davis, J., Desfontaines, D., Fabrikant, A. and Wilson, R. J. (2020). Google COVID-19 community mobility reports: anonymization process description (version 1.1). arXiv preprint arXiv:2004.04145, 2020.
Roy, A., and Kar, S. Nature of transmission of COVID-19 in India. Medrxiv, 2020-04, 2020.
N. Kumar, S. Susan, “COVID-19 Pandemic Prediction using Time Series Forecasting Models,” 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020, pp. 1–7, doi: 10.1109/ICCCNT49239.2020.9225319.
Acker, A., and Chaiet, M, The weaponization of web archives: Data craft and COVID-19 publics. Good Systems-Published Research, 2020.
Wang, L., Li, R., Zhu, J., Bai, G., and Wang, H, When the open source community meets covid-19: Characterizing covid-19 themed github repositories. arXiv preprint arXiv:2010.12218, 2020.
Nadeem Ahmed, Regio A. Michelin, WanliXue, SushmitaRuj, Robert Malaney, Salil S. Kanhere, ArunaSeneviratne, Wen Hu, Helge Janicke, Sanjay K. Jha, “A Survey of COVID-19 Contact Tracing Apps”, 2020, IEEE Access.
B Padmaja, Madhu Bala Myneni, E Krishna Ro Patro, “A Comparison on Visual Prediction Models for MAMO (Multi Activity-Multi Object) Recognition using Deep Learning,” in Journal of Big Data, Springer, 2019.
M. Elkhodr, O. Mubin, Z. Iftikhar, M. Masood, B. Alsinglawi, S. Shahid and F. Flnajjar, “Technology, privacy, and user opinions of COVID-19 mobile apps for contact tracing: Systematic search and content analysis,” Journal of Medical Internet Research, vol. 23, no. 2, e23467, 2021.
Sarah Zabel, Michael P. Schlaile, Siegmar Otto, Breaking the chain with individual gain? Investigating the moral intensity of COVID-19 digital contact tracing, Computers in Human Behavior, Volume 143, 2023, 107699, ISSN 0747-5632.
Chopdar, P. K., Adoption of Covid-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator. Health Policy and Technology, 11(3), Article 100651, 2022.
Eugene Y Chan and Najam U Saqib, Privacy concerns can explain unwillingness to download and use contact tracing apps when COVID-19 concerns are high. Computers in Human Behavior 119, 2021, 106718.
Alexei Tretiakov and Inga Hunter. 2021. User Experiences of the New Zealand COVID Tracer App: Thematic Analysis of Interviews, 2021.
Y. Bengio, D. Ippolito, R. Janda, M. Jarvie, B. Prud’homme, J.-F. Rousseau, A. Sharma and Y. W. Yu, “Inherent privacy limitations of decentralized contact tracing apps,” Journal of the American Medical Informatics Association, vol. 28, no. 1, pp. 193–195, 2021.
Michael D. Dzandu, Antecedent, behaviour, and consequence (a-b-c) of deploying the contact tracing app in response to COVID-19: Evidence from Europe, Technological Forecasting and Social Change, Volume 187, 122217, 2023.
Sarah Zabel, Michael P. Schlaile, Siegmar Otto, Breaking the chain with individual gain? Investigating the moral intensity of COVID-19 digital contact tracing, Computers in Human Behavior, Volume 143, 107699, 2023.
Momeng Liu, Zeyu Zhang, Wenqiang Chai, Baocang Wang, Privacy-preserving COVID-19 contact tracing solution based on blockchain, Computer Standards & Interfaces, Volume 83, 103643, 2023.