Data privacy during pandemics: a systematic literature review of COVID-19 smartphone applications

Research questions

We aim to answer, at the end of this systematic literary review, three research questions:

1. What techniques are proposed to protect users’ privacy in digital surveillance?

2. How does the law protect users’ privacy in COVID-19 applications?

3. How do different entities contribute to preserving individuals’ health privacy?

Search strategy

The search strategy consists of three steps, as defined below.

Inclusion and exclusion criteria

After exploring all the libraries’ engines, we obtained 808 papers, which decreased to 565 papers after duplication removal. We then filtered the papers by reading titles, keywords, and abstracts. This process yielded 60 papers. We used the inclusion and exclusion criteria to filter the papers as shown in Table 1.

Quality assessment criteria

The 60 papers from the previous step were read thoroughly and checked against quality assessment questions as in Table 2. All questions were weighted to 1 point for Yes, 0 for No, and 0.5 for partial. Papers that scored 2 points or more were included in the final collection as shown in Fig. 2. Papers scoring less than 2 points were reviewed by a team member and re-scored. Papers still scoring less than 2 were excluded.

Document search strategy

After assembling the final collection of papers, we assigned each paper to its origin library. As shown in Table 3, some libraries have no papers in the final collection.

Snowballing

For better comprehensiveness of papers, we conducted forward snowballing to cover all the papers related to our topic. After reading the titles of the papers’ references, we obtained 13 papers in the first stage. After reading the abstracts, the number decreased to 6 papers. We then read the papers in their entirety and performed quality assessment, yielding five papers. This process was repeated until no more results were produced; resulting in 40 papers. Figure 3 shows the total number of papers from each data source and Fig. 4 illustrates the selection process.

Proposed techniques to protect users’ privacy in digital surveillance

Based on how applications collect and share information, app architecture is classified into two different models: centralized and decentralized. These models differ in their approaches to protecting users’ privacy and the anonymity degree. In the centralized model, health authorities and governments collect data from individuals regardless of whether they are healthy or diagnosed with COVID-19, mapping the collected information to everyone uniquely in a central server. This approach effectively controls cases if it is widely used, as it provides a comprehensive view. However, the model does not ensure users’ privacy because there is no control over data sharing. In contrast, the decentralized model does not offer public control, as it does not have a central server for data storage. Instead, individuals who tested negative or did not test at all store their data locally on their devices and can check whether they were in touch with infected people through public platforms that have already gone through a data anonymization cycle. The sections below discuss the models in detail (Wang & Liu, 2020).

Centralized contact-tracing

As abovementioned, there are two different technologies to collect individuals’ information used in centralized and decentralized models. This section will discuss the differences between using GPS and Bluetooth technologies in the centralized model from a privacy perspective. In centralized GPS-based applications, the user's places are collected and shared with the authorities’ servers (Joo & Shin, 2020). In centralized Bluetooth-based applications, data are collected by creating random tokens at different times and exchanging with other users if they happen within 6 feet. Later, each user’s phone number and token are sent to health authorities, informing the people that person has countered within the past 2 weeks if the user is found to be infected. A centralized application using both GPS and Bluetooth technologies to collect users’ information has been deployed in India (Wang & Liu, 2020).

The main examples of centralized contact-tracing applications are Alipay Health Code used in China and Self-Quarantine Safety Protection used in South Korea (Joo & Shin, 2020), both of which are GPS-based, and TraceTogether, a Bluetooth-based app used in Singapore (Wang & Liu, 2020). These apps have helped governments limit the spread of COVID-19. In the centralized model, control is vested in governments and authorities, since they can trace all users’ health status and the number of infected people; this makes such apps more accurate and efficient in quickly understanding the situation. Thus, they allow for better control of the virus (Riemer et al., 2020).

When comparing Asian countries with some European countries to adopt centralized contact-tracing apps, Asia have shown better control over the virus’ spread than Europe. The reason can be attributed to Asian citizens’ willingness to sacrifice privacy in the interest of public health (Cha, 2020), whereas in some European countries, such as France with the Stop-COVID app, citizens refused to use contact-tracing, triggering a massive virus spread and losing control (Rowe, 2020). In other words, using the most effective approach does not guarantee the best results because civic readiness for and app’s acceptance are crucial influencing factors.

Centralized applications have shown great potential to limit viral spread; however, users’ concerns about privacy and how their data are exposed and controlled by government and health authorities have caused stress and motivated them to avoid using contact-tracing apps. This stress is known as technostress: technology-caused anxiety and negative emotions. The Alipay Health Code used in China is a great example of how collecting users’ information increases stress and anxiety. Considering its different information collection methods, like drones, GPS, QR codes, and CCTV (Joo & Shin, 2020), it can be agreed it is difficult to trust the government and health authorities when they do not state how they protect and process the collected information (Vitak & Zimmer, 2020; Nabity-Grover, Cheung & Thatcher, 2020).

Moreover, using centralized techniques has raised some privacy risks associated with each technology. For GPS-based applications, the authority collects information from all users regardless of infection status and broadcasts all the locations the user has visited recently when someone tests positive, making it hard to maintain infected users’ confidentiality. Bluetooth-based applications are also vulnerable to these stated risks. Besides, there is a risk the authorities will connect with another database via users’ phone numbers and access sensitive information if deemed necessary (Wang & Liu, 2020). Furthermore, some people think the centralized contact-tracing approach infringes freedom and carries long-term risks like records of the information collected during the pandemic even after ending (Rowe, 2020).

However, one study suggests forcing citizens to use contact-tracing apps regardless of privacy concerns applying a framework complete with rules and policies punishing non-users will enhance control and limit the virus’ spread (Riemer et al., 2020).

Decentralized contact-tracing

Based on the aforementioned problems with centralized contact-tracing apps, the importance of using decentralized platforms offering a higher degree of privacy regarding individuals’ data has emerged (Cho, Ippolito & Yu, 2020). Decentralized contact-tracing provides more privacy because, unlike in the centralized model, it has mechanisms to verify privacy and use public and private keys and digital signatures (Skoll, Miller & Saxon, 2020)

The solutions provided in the distributed approach can utilize either GPS or Bluetooth, both offering a degree of privacy. With Bluetooth, data are transferred in a phone-to-phone transaction: location data are sent directly without an HTTP connection. Compared to GPS, Bluetooth is more private, as GPS applications share data via HTTP protocol. However, both can offer more privacy when used with blockchain (Garg et al., 2020).

BayesCOVID is a GPS approach combining contact-tracing, symptom tracking, and Bayesian network. This approach gives the user a choice, since it provides a user contract that enhances the application’s utility (McLachlan et al., 2020).

One important solution is the Apple/Google Bluetooth approach that prioritized individuals’ privacy by using Privacy-Pre-Serving Proximity Tracing (DP-3 T) with both the SARS and the Middle East Respiratory Syndrome (MERS) outbreaks. This method provides a high degree of privacy, since it uses Bluetooth, which offers more location privacy than GPS (Fahey & Hino, 2020; Wang & Liu, 2020) and avoids storing data (location, users’ identities, contacts) in archives. This approach is very secure and provides a high degree of privacy. Unfortunately, it taxes on smartphones’ battery life, which is a drawback for users (Fahey & Hino, 2020).

However, offering users control is encouraging. Specifically, users can control which data are being transferred and which are not. This approach uses two models: simple data transfer and distributed computation. It also ensures self-awareness, which will encourage users to risk sharing data (Nanni et al., 2020).

Furthermore, the decentralized technique provides a high degree of privacy because it uses two-factor authentication blockchain. It also uses personal data without storing it in a database. It accomplishes this with encoded data storage, which can be accessed with users’ consent. This approach cryptographically signs user data and stores it. When the data are deleted, the hash will redirect to a null reference called “orphan hash.” This approach has the advantage of encouraging users to trust contact-tracing applications, which will help restore normal life sooner (Eisenstadt et al., 2020).

Based on the outcome of this SLR and our findings, we present the following future considerations and directions for contact tracing apps and related technologies in the fight against COVID-19 and future pandemic outbreaks that are worth investigating and implementing to encourage adoption by the wider population:

Utilizing privacy-protecting technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is suggested to help analyze the level of infection by viruses through identification of infected areas, tracing, and monitoring infected people (Vaishya et al., 2020; Ahmed et al., 2020). Other researchers (Yang et al., 2020; Ting et al., 2020) propose the use of the Internet of Things (IoT) and thermal imaging devices (Chamberlain et al., 2020; Mohammed et al., 2020) to track positive cases and control the wide spread of COVID-19 virus. Additionally, some studies proposed use of a privacy-preserving contact-tracing scheme through blockchain-based medical applications (Zhang et al., 2021; Chang & Park, 2020).

Governments, decision makers, and public health authorities must implement a proper feedback system throughout contact tracing apps deployment phases to gain public trust and increase adoption levels. It must be very clear to users what data is being collected, who is accessing the data, and how it is being used. It is key to study and understand human behavior throughout the design and development phases of the apps before their actual implementation. Authorities could implement multiple models and theories, such as the technology acceptance model, diffusion of innovation model, and motivation theory, to study the acceptance and usage level of future contact tracing technologies (Lucivero et al., 2020; He, Zhang & Li, 2021; Sharma et al., 2020).

Lastly, it is necessary to focus on privacy and data transformation while minimizing data collection and access to reduce contact-tracing privacy concerns (Fahey & Hino, 2020).

Privacy protection laws for COVID-19 applications

Data privacy, also known as information privacy, is a subset of data security that focuses on data management while complying with data security guidelines. The essence of data privacy is how data should be collected, stored, managed, and shared. Practical data privacy issues often revolve around whether or not data is shared with third parties, how it is shared, and how data is lawfully collected and preserved. With the rise of the digital economy, one of the most difficult issues for organizations to address is data privacy. As a result, adhering to a data privacy policy and managing the data that is required is crucial in order to gain users’ confidence.

With the individual as the major character, data privacy involves not only the proper handling of data but also the public expectations about privacy. Individuals are entitled to privacy and control over their personal information. Procedures for safely and securely keeping, processing, acquiring, and sharing personal data must be implemented at all times.

Consumers’ data protection and privacy are vital in today’s technology era, therefore governments, healthcare providers, and business groups have been using digital tracking to keep COVID-19 outbreaks under control. Although this method has the potential to minimize pandemic transmission, it has significant privacy implications (Sharma et al., 2020; Monroe, Tazi & Das, 2021).

There is tension between privacy and information disclosure, and the data’s privacy managed by digital health apps must be maintained to limit the virus spread. There are two types of COVID-19 data. The first type consists of cases and special medical data, such as disease statistics, medical sources, and the history of cases in contact with the disease. The second type is data related to government-imposed containment policies and health measures, such as social distancing and quarantine.

A problem has emerged in the data managed in COVID-19 applications and on social media: data and information are released from various sources, confusing the public about what information is true and which sources are credible. The main sources of COVID-19 data and information are government agencies, local governments, health authorities, and international organizations such as the World Health Organization (WHO). The data and information administered by COVID-19 digital health apps has helped limit the epidemic spread through disclosure, specifically facilitating people’s understanding of the containment measures. This has been effective because people are more willing to comply with containment measures when they understand the issues raised around the virus. Additionally, this will result in raising awareness and acceptance of policies, and an enhanced sense of safety.

Finally, information disclosure reshapes people’s perceptions of the epidemic and containment measures, enabling the public to overcome difficulties and limit the virus’ spread. However, disclosure should be performed only with individual consent (Fu, Ma & Wu, 2020).

Issues

As mentioned before, employing digital surveillance technologies to contain the virus raised several privacy issues related to the use, storage, and manipulation of collected personal information. Individuals’ concerns about their privacy prevented them from using such apps, which obviously affected the tracing process considerably. Those individuals questioned the integrity of the data collection and utilization processes and whether the data will be anonymous, temporarily stored, or open to public use. Figure 6 illustrates the most important privacy issues of contact-tracing apps.

To fulfill privacy guidelines to the highest degree, tracing apps should clearly reveal for which purpose the data will be used, and whom will have access to it and control it. In addition, the data should only be stored by authorized agencies. Health care agencies must clarify what will happened to the data in the future after the pandemic is over. All tracing apps must comply with the international privacy standards to minimize public privacy concerns.

On the other side, these issues can be eliminated from technological perspective by applying more secure and private models. Two popular models have been used in designing tracing apps, which are centralized and decentralized. After comparing these two models, the decentralized model proved to be more secure and reliable, especially when used with Bluetooth technology.

Entities contributing to preserving privacy of healthcare applications

In 1996, a law obliged the health authorities to protect patients’ privacy, disclosing their data only in high-risk cases and guaranteeing information privacy even during data exchange. Furthermore, integration between citizens’ data and the health authorities will help identify infected patients and restrict their interaction with healthy people. Patients’ privacy is one of the most important topics raised during the COVID-19 pandemic.

COVID-19’s proliferation has created unique circumstances that have prompted a change toward telemedicine infrastructure adoption. Telemedicine has become an important part of clinical care delivery, and many medical institutions report a significant increase in the use of telemedicine. For example, One Medical Center in New York City, witnessed a major increase in urgent care virtual visits from 102 per day pre-COVID-19, to 802 per day post-COVID-19. As the transition to telemedicine progresses, new problems and dangers have emerged, especially in the areas of information security and privacy.

The Privacy Rule, issued in December 2000 by the US Department of Health and Human Services (HHS), protects the privacy of individually identifiable health information. In addition, The European Union, passed a data privacy legislation to protect patient’s personal health information, and has one of the most effective data protection and privacy policies in the world. However, government agencies around the world have warned that the risk of cyberattacks against healthcare departments and institutions researching COVID-19 is increasing since the pandemic started (Jalali, Landman & Gordon, 2021).

There are many applied methodologies when it comes to the type of data health officials are sharing with the public. In the US, the local government in Los Angeles County provides an estimated age distribution of patients, and a breakdown of the number of cases in more than 140 cities and communities. However, residents in Florida are given much more information, including the cities affected, the number of people tested, the age distribution of cases, and the number of cases in nursing homes.

In response to the COVID-19 pandemic, the Indian Ministry of Health and Family Welfare issued guidelines for the mandatory notification of information for COVID-19 patients, allowing the government to enact any regulations it deems necessary to prevent the outbreak or spread of such epidemics. The Indian government requires doctors to report COVID-19 cases and suspected cases to designated government agencies, and the government agencies can then respond appropriately to limit the disease spread based on the information provided by the health care professionals (Fu, Ma & Wu, 2020; Shekhawat et al., 2020).

Digital health apps are available in Apple Store and Google Play App Store that contain more than 318,000 available applications which are updated daily, to comply with the latest policies announced by the health authorities.

The Federal Data Protection Act (FADP) provides a comprehensive framework dealing with data protection using defined principles. It insists on securing individuals’ privacy and provides protection measures. According to FADP, patient data are considered sensitive and require additional privacy. Each user has to give consent for the health authorities to use their data for health purposes. Therefore, the applications have to grant the user the right to revoke, update, and remove the data (Vokinger et al., 2020).

The COVID-19 pandemic has shed light on digital health applications but ignored their data privacy. Blockchain technology appears to be an ideal solution to secure and authenticate certificates, health and medical records, and prescriptions, while preserving privacy (De, Pandey & Pal, 2020) Doctors may disclose patients’ information to competent authorities under specific circumstances for society’s greater interest. Quarantine and social isolation measures imposed by the health authorities have effectively limited viral spread. Thus, it is important to collect individuals’ information via contact-tracing apps (Labs & Terry, 2020; Shekhawat et al., 2020).

In conclusion, tracing applications help to control the spread of the COVID-19 pandemic. Governments and health authorities developed these apps based on many technologies and different models. The aim of these apps is to monitor the infected individuals and keep track of the public status. Some countries had followed international laws and local regulations to protect users’ privacy while designing and developing these apps. However, other countries did not comply with these requirements, which resulted in privacy breaches. Nevertheless, there was no clear guidelines regarding the disclosure of using the personal data in tracing apps. However, even with the privacy limitation in tracing apps, WHO stated that they had effectively helped in containing the pandemic and slowing down its spread.

COVID-19 has put forward privacy concerns in many fields, and this systematic literature review has discussed it from three perspectives: the public’s privacy in using contact-tracing apps, laws and policies that should be followed to protect users’ privacy and digital authorities’ strategies for dealing with data privacy. The classifications of the included literature is illustrated in Fig. 7.

Implications

Many persons avoid using COVID-19 apps due to privacy concerns about location and health data (De, Pandey & Pal, 2020; Fahey & Hino, 2020). To prevent this issue, governments should develop policies ensuring individual data privacy rights, encouraging people to trust these apps and provide their information voluntarily (Lee & Lee, 2020).

Another implication: not all citizens have Internet access. The solution is providing universal Internet so that everyone can access COVID-19 apps. Moreover, some individuals do not have their own devices, raising the issue these apps may not cover all citizens. This challenge can be surmounted by assigning one account per family to reach the highest number of users (De, Pandey & Pal, 2020; Rowe, 2020).

Lastly, COVID-19 problems are widespread since it is a new virus, but researchers are trying to address each concern to reduce the virus’ effects and minimize its spread (De, Pandey & Pal, 2020; Hendl, Chung & Wild, 2020)

Limitations and future work

COVID-19 has revealed many limitations in many areas such as information management and data privacy; therefore, digital surveillance has become more important than ever. Artificial intelligence, big data, the Internet of Things, and GPS have been recognized as paramount technologies in developing COVID-19 contact-tracing apps (Mbunge, 2020; Joo & Shin, 2020; Fahey & Hino, 2020).

Privacy protection is an important issue. In the context of digital health and the COVID-19 epidemic, within a framework for evaluating applications from epidemiological and legal perspectives, solutions are designed to obtain useful information with several limitations, including preventing sharing sensitive personal information (Sharma et al., 2020; Nanni et al., 2020; Vokinger et al., 2020).

The studies did not cover all classifications of COVID-19 research's keywords, and they are also restricted to specific countries’ cultures and policies. Moreover, the studies did not consider the differences in values and cultural and political aspects of the countries using tracking applications. Additionally, no study has discussed contact-tracing applications developed after April 30, 2020 (Garg et al., 2020; Trang et al., 2020; Islam et al., 2020; Kumar, Shahrabani & Das, 2020). For the future, Garg expects to develop an RFID solution and aims to reduce the cost of scaling the RFID range (Garg et al., 2020).

The COVID-19 pandemic is a recent one, hence applications in this field are limited. The study found that a limited number of mobile applications were developed, which will be used as a benchmark for future applications' specifications and learn more about users' interactions on different national app platforms. That result makes a significant contribution to health institutes and health practitioners (Trang et al., 2020; Islam et al., 2020).

During epidemics, it has been suggested it is prudent to allow for some loss of privacy and place trust in smart technologies to help fight deadly, invisible creatures. With the continuing spread of COVID-19, Singapore will continue to deploy technological tools and interventions (Lee & Lee, 2020).

A limitation of future research on an ultimate dependent variable is the adoption of COVID-19 applications recommendations for pre- and post-testing in future studies. Emphasis should be placed on collecting data about infectious diseases, ensuring public health and that epidemiological surveillance technology features are ethical and reflective of fair values, and reducing the vulnerability of at-risk individuals (Sharma et al., 2020; Skoll, Miller & Saxon, 2020; Whaiduzzaman et al., 2020; Hendl, Chung & Wild, 2020).

Moreover, developing and improving the efficiency and effectiveness of information systems and technology in organizations as well as monitoring people’s safety and privacy in the fight against COVID-19 (Mbunge, 2020; O’Leary, 2020; Wang & Liu, 2020) are essential. Additionally, expanding anti-snooper privacy safeguards, imposing usage restrictions in contact-tracing, and adding a private messaging system will enhance overall privacy. There have been discussions about creating an application to track contacts directly using Bluetooth (Cho, Ippolito & Yu, 2020; Vitak & Zimmer, 2020).