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Enabling User-centered Privacy Controls for Mobile Applications: COVID-19 Perspective

Published: 30 January 2021 Publication History

Abstract

Mobile apps have transformed many aspects of clinical practice and are becoming a commonplace in healthcare settings. The recent COVID-19 pandemic has provided the opportunity for such apps to play an important role in reducing the spread of the virus. Several types of COVID-19 apps have enabled healthcare professionals and governments to communicate with the public regarding the pandemic spread, coronavirus awareness, and self-quarantine measures. While these apps provide immense benefits for the containment of the spread, privacy and security of these digital tracing apps are at the center of public debate. To address this gap, we conducted an online survey of a midwestern region in the United State to assess people’s attitudes toward such apps and to examine their privacy and security concerns and preferences. Survey results from 1,550 participants indicate that privacy/security protections and trust play a vital role in people’s adoption of such apps. Furthermore, results reflect users’ preferences wanting to have control over their personal information and transparency on how their data is handled. In addition, personal data protection priorities selected by the participants were surprising and yet revealing of the disconnect between technologists and users. In this article, we present our detailed survey results as well as design guidelines for app developers to develop innovative human-centered technologies that are not only functional but also respectful of social norms and protections of civil liberties. Our study examines users’ preferences for COVID-19 apps and integrates important factors of trust, willingness, and preferences in the context of app development. Through our research findings, we suggest mechanisms for designing inclusive apps’ privacy and security measures that can be put into practice for healthcare-related apps, so that timely adoption is made possible.

References

[1]
Icek Ajzen. 2011. The theory of planned behaviour: Reactions and reflections. Psychol Health. 26, 9 (2011), 1113--27.
[2]
Faiz Anuar and Ulrike Gretzel. 2011. Privacy concerns in the context of location-based services for tourism. In Proceedings of the 18th Annual Conference of the International Federation for Information Technologies in Travel and Tourism (ENTER’11).
[3]
Kakoli Bandyopadhyay and Katherine A. Fraccastoro. 2007. The effect of culture on user acceptance of information technology. Commun. Assoc. Info. Syst. 19, 1 (2007), 23.
[4]
Gaurav Bansal, David Gefen, et al. 2010. The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decis. Supp. Syst. 49, 2 (2010), 138--150.
[5]
Mahmoud Barhamgi, Charith Perera, Chirine Ghedira, and Djamal Benslimane. 2018. User-centric privacy engineering for the internet of things. IEEE Cloud Comput. 5, 5 (2018), 47--57.
[6]
Mahmoud Barhamgi, Mu Yang, Chia-Mu Yu, Yijun Yu, Arosha K. Bandara, Djamal Benslimane, and Bashar Nuseibeh. 2017. Enabling end-users to protect their privacy. In Proceedings of the ACM Asia Conference on Computer and Communications Security. 905--907.
[7]
Erlend Bergen, Dag F. Solberg, Torjus H. Sæthre, and Monica Divitini. 2018. Supporting the co-design of games for privacy awareness. In Proceeding sof the International Conference on Interactive Collaborative Learning. Springer, 888--899.
[8]
David M. Blei. 2012. Probabilistic topic models. Commun. ACM 55, 4 (2012), 77--84.
[9]
Kaitlin R. Boeckl and Naomi B. Lefkovitz. 2020. NIST privacy framework: A tool for improving privacy through enterprise risk management, version 1.0. NIST Publication.
[10]
Samuel Brack, Leonie Reichert, and Björn Scheuermann. 2020. Decentralized contact tracing using a DHT and blind signatures. IACR Cryptol. ePrint Arch. 2020 (2020), 398.
[11]
Diego Buenaño-Fernandez, Mario González, David Gil, and Sergio Luján-Mora. 2020. Text mining of open-ended questions in self-assessment of university teachers: An LDA topic modeling approach. IEEE Access 8 (2020), 35318--35330.
[12]
Ann Cavoukian et al. 2009. Privacy by design: The 7 foundational principles. Info. Privacy Commiss. Ont. Can. 5 (2009). https://www.ipc.on.ca/wp-content/uploads/Resources/7foundationalprinciples.pdf.
[13]
Ann Cavoukian, Angus Fisher, Scott Killen, and David A. Hoffman. 2010. Remote home healthcare technologies: How to ensure privacy? Build it in: Privacy by design. Ident. Info. Soc. 3, 2 (2010), 363--378.
[14]
Justin Chan, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob Sunshine, et al. 2020. Pact: Privacy sensitive protocols and mechanisms for mobile contact tracing. Retrieved from https://arXiv:2004.03544.
[15]
Hyunghoon Cho, Daphne Ippolito, and Yun William Yu. 2020. Contact tracing mobile apps for COVID-19: Privacy considerations and related trade-offs. Retrieved from https://arXiv:2003.11511.
[16]
Kevin Anthony Hoff and Masooda Bashir. 2015. Trust in automation: Integrating empirical evidence on factors that influence trust. Hum. Fact. 57, 3 (2015), 407--434.
[17]
Marcello Ienca and Effy Vayena. 2020. On the responsible use of digital data to tackle the COVID-19 pandemic. Nature Med. 26, 4 (2020), 463--464.
[18]
Nirmal Kandel, Stella Chungong, Abbas Omaar, and Jun Xing. 2020. Health security capacities in the context of COVID-19 outbreak: An analysis of International Health Regulations annual report data from 182 countries. Lancet 395, 10229 (2020), 1047--1053.
[19]
Gabriel Kaptchuk, Eszter Hargittai, and Elissa M. Redmiles. 2020. How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt. Retrieved from https://arXiv:2005.04343.
[20]
Bandana Kar, Rick C. Crowsey, and Joslyn J. Zale. 2013. The myth of location privacy in the United States: Surveyed attitude versus current practices. Profession. Geogr. 65, 1 (2013), 47--64.
[21]
Douglas J. Leith and Stephen Farrell. 2020. Coronavirus contact tracing app privacy: What data is shared by the singapore opentrace app. Retrieved from https://www.scss.tcd.ie/Doug.Leith/pubs/opentrace_privacy.pdf.
[22]
Joseph K. Liu, Man Ho Au, Tsz Hon Yuen, Cong Zuo, Jiawei Wang, Amin Sakzad, Xiapu Luo, and Li Li. 2020. Privacy-preserving COVID-19 contact tracing app: A zero-knowledge proof approach. IACR Cryptol. ePrint Arch. 2020 (2020), 528.
[23]
Anna U. Morgan, Mohan Balachandran, David Do, Doreen Lam, Andrew Parambath, Krisda H. Chaiyachati, Nancy M. Bonalumi, Susan C. Day, Kathleen C. Lee, and David A. Asch. 2020. Remote monitoring of patients with Covid-19: Design, implementation, and outcomes of the first 3,000 patients in COVID watch. NEJM Catal. Innovat. Care Deliv. 1, 4 (2020).
[24]
N. Nair. 2001. Childhood tuberculosis: Public health and contact tracing. Paediatr. Resp. Rev. 2, 2 (2001), 97--102.
[25]
Steve Quirolgico, Jeffrey Voas, Tom Karygiannis, Christoph Michael, and Karen Scarfone. 2015. Vetting the Security of Mobile Applications. U.S. Department of Commerce, National Institute of Standards and Technology.
[26]
Julie M. Robillard, Aaron W. Li, Shilpa Jacob, Dan Wang, Xin Zou, and Jesse Hoey. 2017. Co-creating emotionally aligned smart homes using social psychological modeling. In Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction. 1--6.
[27]
Tushar Kanti Saha Santa Maria Shithil and Tanusree Sharma. [n.d.]. A dynamic data placement policy for heterogeneous Hadoop cluster. In 2017 4th International Conference on Advances in Electrical Engineering (ICAEE'17). IEEE, 302--307.
[28]
Oshani Seneviratne and Lalana Kagal. 2014. Enabling privacy through transparency. In Proceedings of the 12th Annual International Conference on Privacy, Security and Trust. IEEE, 121--128.
[29]
Tanusree Sharma, John C. Bambenek, and Masooda Bashir. 2020. Preserving privacy in cyber-physical-social systems: An anonymity and access control approach. [Online]. Retrieved from https://www.ideals.illinois.edu/handle/2142/106049.
[30]
Tanusree Sharma and Masooda Bashir. 2020. Are PETs (privacy enhancing technologies) giving protection for smartphones?--A case study. Retrieved from https://arXiv:2007.04444.
[31]
Tanusree Sharma and Masooda Bashir. 2020. Privacy apps for smartphones: An assessment of users’ preferences and limitations. In Proceedings of the International Conference on Human-Computer Interaction. Springer, 533--546.
[32]
Tanusree Sharma and Masooda Bashir. 2020. Use of apps in the COVID-19 response and the loss of privacy protection. Nature Med. 26 (2020), 1165--1167.
[33]
Lucy Simko, Ryan Calo, Franziska Roesner, and Tadayoshi Kohno. 2020. COVID-19 contact tracing and privacy: Studying opinion and preferences. Retrieved from https://arXiv:2005.06056.
[34]
Daniel J. Solove. 2012. Introduction: Privacy self-management and the consent dilemma. Harv. L. Rev. 126 (2012), 1880.
[35]
Murugiah Souppaya and Karen Scarfone. 2013. Guidelines for managing the security of mobile devices in the enterprise. NIST Spec. Pub. 800 (2013), 124.
[36]
Carmela Troncoso, Mathias Payer, Jean-Pierre Hubaux, Marcel Salathé, James Larus, Edouard Bugnion, Wouter Lueks, Theresa Stadler, Apostolos Pyrgelis, Daniele Antonioli, et al. 2020. Decentralized privacy-preserving proximity tracing. Retrieved from https://arXiv:2005.12273.
[37]
Tyler M. Yasaka, Brandon M. Lehrich, and Ronald Sahyouni. 2020. Peer-to-Peer contact tracing: Development of a privacy-preserving smartphone app. JMIR mHealth uHealth 8, 4 (2020), e18936.
[38]
Xiao Yue, Huiju Wang, Dawei Jin, Mingqiang Li, and Wei Jiang. 2016. Healthcare data gateways: Found healthcare intelligence on blockchain with novel privacy risk control. J. Med. Syst. 40, 10 (2016), 218.

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    Published In

    cover image ACM Transactions on Internet Technology
    ACM Transactions on Internet Technology  Volume 21, Issue 1
    Visions Paper, Regular Papers, SI: Blockchain in E-Commerce, and SI: Human-Centered Security, Privacy, and Trust in the Internet of Things
    February 2021
    534 pages
    ISSN:1533-5399
    EISSN:1557-6051
    DOI:10.1145/3441681
    • Editor:
    • Ling Liu
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 30 January 2021
    Accepted: 01 November 2020
    Revised: 01 October 2020
    Received: 01 July 2020
    Published in TOIT Volume 21, Issue 1

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    Author Tags

    1. COVID-19
    2. Mobile apps
    3. human-centered
    4. privacy 8 security
    5. trust

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    • (2023)Interactive Privacy Management: Toward Enhancing Privacy Awareness and Control in the Internet of ThingsACM Transactions on Internet of Things10.1145/36000964:3(1-34)Online publication date: 21-Sep-2023
    • (2023)Emerging Trends of ICT in Airborne Disease PreventionACM Transactions on Internet Technology10.1145/356478322:4(1-18)Online publication date: 15-Mar-2023
    • (2023)Understanding the adoption of digital conferencing tools: Unpacking the impact of privacy concerns and incident response efficacyComputers & Security10.1016/j.cose.2023.103375132(103375)Online publication date: Sep-2023
    • (2022)Privacy and Security Concerns During the COVID-19 PandemicHandbook of Research on Technical, Privacy, and Security Challenges in a Modern World10.4018/978-1-6684-5250-9.ch011(205-222)Online publication date: 30-Jun-2022
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    • (2022)Public Views on Digital COVID-19 Certificates: a Mixed Methods User StudyProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502066(1-28)Online publication date: 29-Apr-2022
    • (2022)Unpacking Intention and Behavior: Explaining Contact Tracing App Adoption and Hesitancy in the United StatesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501963(1-14)Online publication date: 29-Apr-2022
    • (2022)A Privacy-Assured Data Lifecycle for Epidemic-Handling SystemsComputer10.1109/MC.2021.313878055:8(57-69)Online publication date: 1-Aug-2022
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