ABSTRACT
The SARS-CoV-2 pandemic is a pressing societal issue today. The German government promotes a contract tracing app named Corona-Warn-App (CWA), aiming to change citizens' health behaviors during the pandemic by raising awareness about potential infections and enable infection chain tracking. Technical implementations, citizens' perceptions, and public debates around apps differ between countries, e. g., in Germany there has been a huge discussion on potential privacy issues of the app. Thus, we analyze effects of privacy concerns regarding the CWA, perceived CWA benefits, and trust in the German healthcare system to answer why citizens use the CWA. In our initial conference publication at ICT Systems Security and Privacy Protection - 37th IFIP TC 11 International Conference, SEC 2022, we used a sample with 1752 actual users and non-users of the CWA and and support for the privacy calculus theory, i. e., individuals weigh privacy concerns and benefits in their use decision. Thus, citizens privacy perceptions about health technologies (e. g., shaped by public debates) are crucial as they can hinder adoption and negatively affect future fights against pandemics. In this special issue, we adapt our previous work by conducting a second survey 10 months after our initial study with the same pool of participants (830 participants from the first study participated in the second survey). The goal of this longitudinal study is to assess changes in the perceptions of users and non-users over time and to evaluate the influence of the significantly lower hospitalization and death rates on the use behavior which we could observe during the second survey. Our results show that the privacy calculus is relatively stable over time. The only relationship which significantly changes over time is the effect of privacy concerns on the use behavior which significantly decreases over time, i. e., privacy concerns have a lower negative effect one the CWA use indicating that it did not play such an important role in the use decision at a later point in time in the pandemic. We contribute to the literature by introducing one of the rare longitudinal analyses in the literature focusing on the privacy calculus and changes over time in the relevant constructs as well as the relationships between the calculus constructs and target variables (in our case use behavior of a contact tracing app). We can see that the explanatory power of the privacy calculus model is relatively stable over time even if strong externalities might affect individual perceptions related to the model.
ABSTRACT
In tourism, biometrics have become a major technology application for user authentication in premises such as hotels and airports. The main reasons of utilizing biometrics for access control are convenience, high security, and reliability. Biometrics refer to any human trait, for instance, fingerprint, retina, palm print, and voice, which is used to establish an identity. It is already used extensively in many airports worldwide to facilitate automated identity verification, while the backbone is linked to police criminal databases, which enables identification of possible terrorists, victims of human trafficking, or tourists with past convictions. Additionally, many hotels have substituted traditional door locks or card access with biometrics for its convenience. However, there are some concerns associated with biometric applications, for instance, information privacy, physical privacy, and contamination fears. Tourists worry whether the storage mechanism of biometric data is safe from hacking and leakage to terrorist organizations. If biometric data were compromised, tourists would have to surrender different biometric identifiers because biometric features are irrevocable (permanent). Further, fear of contamination can be much more apparent on touch-based biometric applications such as fingerprint scanners. This concern is aggravated especially during pandemic outbreaks such as COVID-19, Ebola, and SARS. In a nutshell, this chapter aims to provide a holistic perspective on potential issues of biometrics in the tourism industry. © Springer Nature Switzerland AG 2022.
ABSTRACT
Technology played a central role during the pandemic for communications and services. It was also touted as a potential solution to control the spread of COVID-19 via proximity tracing applications, also known as contact tracing (CT) apps worldwide. In non-mandated settings, however, these apps did not attain popularity. Privacy concerns were highlighted as one reason. We explored how family perceptions of CT apps can affect the family's use of such apps. We surveyed parent-teen dyads twice over a 5-month period. We analyzed parent-teen perceptions of each other's intentions and use of CT apps at time 1 and 2, exploring changes over time. Parents' use intentions were influenced by their and their teens' perceptions of the benefits but not privacy concerns. Teen intentions were influenced by their own perceptions of benefits, not their parent's, and their parent's concerns for the family. Intentions always influenced usage, including intentions at time 1 influencing use at time 2, demonstrating a longitudinal effect of intentions on usage existed for parents and teens. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
Privacy concerns around sharing personal health information are frequently cited as hindering COVID-19 contact tracing app adoption. We conducted a nationally representative survey of 304 adults in the United States to investigate their attitudes towards sharing two types of COVID-19 health status (COVID-19 Diagnosis, Exposure to COVID-19) with three different audiences (Anyone, Frequent Contacts, Occasional Contacts). Using the Internet User's Information Privacy Concern (IUIPC) scale, we were able to identify the effect of different types of privacy concerns on sharing this information with various audiences. We found that privacy concerns around data Collection predicted lower willingness to share either type of health status to all of these audiences. However, desire for Control and for Awareness of data practices increased willingness to share health information with certain audiences. We discuss the implications of our findings. © 2022 Owner/Author.
ABSTRACT
PurposeThe purpose of this paper is to explore the reaction of Pakistani citizens toward online privacy in the context of the Pakistan Government's COVID-19 app privacy breach. Design/methodology/approachThe authors implemented supervised and unsupervised machine learning methods, that is, topic modeling and sentiment analysis on Google app store user review data. FindingsThere was no visible concern shown by the citizens toward the COVID-19 app privacy breach, even though it was well highlighted in the news. Other studies have also indicated that concern for online privacy remains low in developing countries, which aligns with the findings of this paper. Originality/valueGlobally COVID-19 apps have been a cause of concern in terms of public privacy. To the best of the authors' knowledge, this paper is the first in the Pakistani context to show the impact of a well-document privacy breach of a government COVID-19 app.
ABSTRACT
The emergence of mobile financial technology (mobile fintech) services raises numerous public concerns regarding privacy issues;consequently, researchers in mobile technology acceptance have focused on consumers' privacy self-disclosure behaviors under the usual scenario. However, there is still a lack of understanding on how external influences, such as a public health crisis, affect consumers' privacy decision-making process. Therefore, in this article, we examine the effects of privacy- and pandemic-related antecedents on mobile fintech users' information self-disclosure behavior during the coronavirus disease 2019 pandemic. The present research adopts a self-administered questionnaire with 712 effective responses for data collection and a two-stage partial least squares-structural equation modeling-artificial neural network (PLS-SEM-ANN) approach to test the theoretical lens proposed. The results indicate that the significant structural paths in the model are consistent with the proposed hypotheses and existing literature. Surprisingly, face-to-face avoidance (FFA) does not significantly influence consumers' self-disclosure willingness. Infection severity and infection susceptibility were insignificant with FFA. The present research is the first to investigate consumers' privacy-related behavior via integrating the privacy-calculus framework with control agency theory. This research focuses on consumers' decision-making during the pandemic, explicitly highlighting the macroenvironment's role in influencing an individual's behavior.
ABSTRACT
Are citizens more willing to share private data in (health) crises? We study citizens' willingness to share personal data through COVID-19 contact tracing apps (CTAs). Based on a cross-national online survey with 6,464 respondents from China, Germany, and the US, we find considerable variation in how and what data respondents are willing to share through CTAs. Drawing on the privacy calculus theory and the trade-off model of privacy and security, we find that during the COVID-19 pandemic, crisis perceptions seem to have only limited influence on people's willingness to share personal data through CTAs. The findings further show that the data type to be shared determines the suitability of the privacy calculus theory to explain people's willingness to transfer personal data: the theory can explain the willingness to share sensitive data, but cannot explain the willingness to share less sensitive data.
ABSTRACT
To counteract the spread of Covid-19, many countries have introduced mobile applications for contact tracing, which raises considerable questions about how these apps protect users' information privacy. Through an exploratory analysis of Covid-19 contact tracing apps being used in Australia, France, Germany, Japan, and New Zealand, we identify normative and technical principles for the design of privacy-sensitive contact tracing apps. Based on a Restricted Access/Limited Control (RALC) account of information privacy, we discuss how the apps protect users' information privacy through limiting access to and allowing users to actively manage their personal information. Our findings illustrate what understanding of information privacy is evident from the various designs of Covid-19 contact tracing apps, and how competing design principles can contribute to users' information privacy. From a practical perspective, our findings can inform the design of contact tracing apps and the development of privacy approaches that can be applied in particular contexts. Our work thus bridges the gap between ethical design guidelines and technical analyses of specific implementations.
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Purpose The coronavirus disease 2019 (COVID-19) epidemic has given an upsurge to online retailing in Pakistan. This shift has escalated the issues about privacy concerns among consumers. Keeping in view the growing concerns, the objective of this study is to investigate customer patronage in online shopping and the role of privacy concerns in this relationship. Design/methodology/approach To generalize the relationship between antecedents and outcomes of privacy concerns, a cross-disciplinary macro model was used. Data were collected through a survey method from the consumers who used credit and debit cards during online shopping. Findings Results show that government regulations have a significant positive relationship with privacy concerns and customer patronage. Privacy concerns are found to have a significant negative relationship with organizational ethical care while customer patronage was found to have a significant positive relationship with organizational ethical care. Customer patronage was also found to have a significant negative relationship with privacy concerns. Privacy concerns mediated the relationship between government regulations and customer patronage, whereas privacy concerns does not mediate the relationship between organizational ethical care and customer patronage. Originality/value The research adds to the existing literature and highlights the customer behavior toward online shopping/e-commerce in developing economies. The research gives a direction to stakeholders to counter privacy concerns and ensure safer e-commerce practices.
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We present an empirical study exploring how privacy influences the acceptance of vaccination certificate (VC) deployments across different realistic usage scenarios. The study employed the privacy framework of Contextual Integrity, which has been shown to be particularly effective in capturing people's privacy expectations across different contexts. We use a vignette methodology, where we selectively manipulate salient contextual parameters to learn whether and how they affect people's attitudes towards VCs. We surveyed 890 participants from a demographically-stratified sample of the US population to gauge the acceptance and overall attitudes towards possible VC deployments to enforce vaccination mandates and the different information flows VCs might entail. Analysis of results collected as part of this study is used to derive general normative observations about different possible VC practices and to provide guidance for the possible deployments of VCs in different contexts. © 2022 Owner/Author.
ABSTRACT
The SARS-CoV-2 pandemic is a pressing societal issue today. The German government promotes a contact tracing app named Corona-Warn-App (CWA), aiming to change citizens’ health behavior during the pandemic by raising awareness about potential infections and enable infection chain tracking. Technical implementations, citizens’ perceptions, and public debates around apps differ between countries, i.e., in Germany there has been a huge discussion on potential privacy issues of the app. Thus, we analyze effects of privacy concerns regarding the CWA, perceived CWA benefits, and trust in the German healthcare system to answer why citizens use the CWA. We use a sample with 1,752 actual users and non-users and find support for the privacy calculus theory, i.e., individuals weigh privacy concerns and benefits in their use decision. Thus, citizens’ privacy perceptions about health technologies (e.g., shaped by public debates) are crucial as they can hinder adoption and negatively affect future fights against pandemics. © 2022, IFIP International Federation for Information Processing.
ABSTRACT
Governments worldwide view contact tracing as a key tool to mitigate COVID-19 community transmission. Contact tracing investigations are time consuming and labour intensive. Mobile phone location tracking has been a new data-driven option to potentially obviate investigative inefficiencies. However, using mobile phone apps for contact tracing purposes gives rise to complex privacy issues. Governmental presentation and implementation of contact tracing apps, therefore, requires careful and sensitive delivery of a coherent policy position to establish citizen trust, which is an essential component of uptake and use. This article critically examines the Australian Government’s initial implementation of the COVIDSafe app. We outline a series of implementation misalignments that juxtapose an underpinning regulatory rationality predicated on the implementation of information privacy law protections with rhetorical campaigns to reinforce different justifications for the app’s use. We then examine these implementation misalignments from Mayer and colleagues’ lens of trustworthiness (1995) and its three core domains: ability, integrity and benevolence. The three domains are used to examine how the Australian Government’s implementation strategy provided a confused understanding of processes that enhance trustworthiness in the adoption of new technologies. In conclusion, we provide a better understanding about securing trustworthiness in new technologies through the establishment of a value consensus that requires alignment of regulatory rationales and rhetorical campaigning. © The Author/s 2021
ABSTRACT
Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users (e.g., avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users’location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design a LEFS algorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further design LSRE that allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the well-known differential privacy based solutions. IEEE