Privacy Risks Research Articles

Perceptions and utilization of tele-orthodontics: a survey of the members of the American Association of Orthodontists

ObjectiveThis study aimed to investigate orthodontists’ utilization and perceptions of tele-orthodontics.Materials and methodsA 30-item online survey was distributed to members of the American Association of Orthodontists (AAO). The questionnaire encompassed topics concerning the orthodontists’ utilization, perceptions, clinical applications, limitations, and concerns regarding tele-orthodontics. Descriptive statistics were employed, and comparisons between responses from users and non-users were conducted  using Wilcoxon rank-sum tests and Fisher’s exact tests.Results152 members completed the survey, (response rate: 2.4%). More than two third of respondents (69.74%) were users of tele-orthodontics. Users were more aligned with the belief that tele-orthodontics facilitates effective communication (mean ± standard deviation (SD) 4.06 ± 0.83 vs. 3.33 ± 0.94, p < 0.001). Both groups agreed on the requirement of patient fees for tele-orthodontic visits (mean ± SD: 3.62 ± 1.11 users vs. 3.74 ± 1.02 non-users, p = 0.659), and on the capability  of the system to reduce unwarranted referrals (p = 0.20). The majority of participants acknowledged  the utility of the system in monitoring aligners’ patients (89% in users vs. 61% in non-users, p < 0.001). Non-users expressed greater concerns regarding privacy risks (mean ± SD: 3.06 ± 0.97 users vs. 3.57 ± 0.86 non-users, p = 0.002). Both groups stressed the significance of obtaining informed consent before utilizing tele-orthodontics.ConclusionsThe widespread acceptance of tele-orthodontics among AAO members was apparent, as demonstrated by their recognition of its effectiveness. There was notable variation in how users and non-users perceived tele-orthodontics. The study's results offer valuable insights into both the potential benefits and drawbacks of incorporating this technology into clinical practice from the clincians' perspective.

The study of social commerce in Generation Z context: the role of social support and privacy risk

PurposeThe recent proliferation of social media platforms has witnessed a growth in social commerce by using social media to facilitate interactivity between customers and vendors. While emergent studies on social commerce are growing, their focus tends to be on millennials and cross-age groups. Given the growth of digital natives in shaping the online shopping experience of the future, we deemed an application to Generation Z necessary and overdue.Design/methodology/approachWe draw on the existing literature and develop a framework to understand social commerce dynamics for digital natives. We employ PLS and CB-SEM to test our proposed model.FindingsOur findings demonstrate the importance of social commerce information sharing activities in facilitating social support, a sense of warmth and belongingness, and online trust for Generation Z platform users. We also investigate the roles of online trust and perceived risk on intention to purchase and find support for both relationships. Finally, we discuss the findings in terms of theoretical and managerial contributions and conclude the study with limitations and future research directions.Originality/valueThis research is unique by using social commerce theory to explore Gen Z platform users. The finding will contribute to information system literature by expanding the social commerce research stream.

Sync fast and solve things—best practices for responsible digital health

Digital health innovation is expected to transform healthcare, but it also generates ethical and societal concerns, such as privacy risks, and biases that can compound existing health inequalities. While such concerns are widely recognized, existing regulatory principles, oversight methods and ethical frameworks seem out of sync with digital health innovation. New governance and innovation best practices are thus needed to bring such principles to bear with the reality of business, innovation, and regulation.To grant practical insight into best practices for responsible digital health innovation, we conducted a qualitative study based on an interactive engagement methodology. We engaged key stakeholders (n = 46) operating at the translational frontier of digital health. This approach allowed us to identify three clusters of governance and innovation best practices in digital health innovation: i) inclusive co-creation, ii) responsive regulation, and iii) value-driven innovation. Our study shows that realizing responsible digital health requires diverse stakeholders’ commitment to adapt innovation and regulation practices, embracing co-creation as the default modus operandi for digital health development. We describe these collaborative practices and show how they can ensure that innovation is neither slowed by overregulation, nor leads to unethical outcomes.

Privacy or trust: understanding the privacy paradox in users intentions towards e-pharmacy adoption through the lens of privacy-calculus model

Purpose This study aims to present a comprehensive investigation into users’ behavioural intentions to use e-pharmacies through the lens of the privacy calculus model. The present study also investigates the effects of perceived benefit, perceived privacy risk, timeliness and perceived app quality on e-pharmacy usage through the mediating role of trustworthiness. Design/methodology/approach The study used a deductive approach and collected data from 338 respondents using the purposive sampling technique. partial least squares structural equation modelling was applied to analyse the data. Findings The findings of the study indicate that perceived benefit, perceived privacy risk, timeliness and perceived app quality do not directly impact users’ behavioural intentions towards e-pharmacy adoption. Instead, it demonstrated that perceived benefit, perceived privacy risk, timeliness and perceived app quality influenced behavioural intention indirectly through the mediating role of trustworthiness Originality/value This study offers valuable insights to entrepreneurs, marketers and policymakers, enabling them to develop regulations, guidelines and policies that cultivate trust, safeguard privacy, ensure prompt services and create an enabling environment for the adoption of e-pharmacies. The present study also contributes to the existing literature by extending the privacy calculus model with the integration of timeliness and perceived app quality to explain users’ adoption behaviour towards e-pharmacy.

Understanding Privacy Risks of High-Accuracy Radio Positioning and Sensing in Wireless Networks

Understanding Privacy Risks of High-Accuracy Radio Positioning and Sensing in Wireless Networks

Emerging Challenges in Privacy Protection with Advancements in Artificial Intelligence

The proliferation of Artificial Intelligence (AI) in various sectors raises significant privacy concerns, demanding a nuanced understanding and strategic approach to privacy protection. This article delves into the intricate challenges of safeguarding privacy in the age of AI, exploring the dynamic interplay between technological advancements and privacy norms. With AI's capacity for extensive data collection and analysis, privacy risks escalate, highlighting the necessity for transparent and ethical data practices. Through examining case studies and regulatory responses, the article underscores the critical role of decentralized AI platforms and robust legal frameworks in ensuring privacy. It advocates for a collaborative effort among stakeholders to balance AI's benefits against privacy rights, aiming for a future where AI technologies are developed and deployed responsibly, with a steadfast commitment to upholding individual privacy and dignity.

Unlock Happy Interactions: Voice Assistants Enable Autonomy and Timeliness

This study examines the effects of three interactive voice assistant (VA) features (responsiveness, ubiquitous connectivity, and personalization) on consumer happiness. An online survey was administered to 316 VA consumers, and the data were analyzed using structural equation modeling with SmartPLS 4 software. The results indicate that VA responsiveness, ubiquitous connectivity, and personalization have significant effects on consumer happiness. This study also provides evidence that consumer happiness is influenced by VA features through the mediating roles of autonomy and timeliness. Notably, perceived privacy risk has a dual effect, negatively affecting happiness but positively moderating the relationship between autonomy and happiness, suggesting a complex interplay between benefits and concerns in user interactions with VAs. This study highlights the need for VA businesses to consider both the enhancing and mitigating factors of technology for user experiences. Furthermore, our findings have significant implications for VA businesses and executives, suggesting that improved interactions through these VA features can better serve consumers and enhance their experiences.

Using a Chatbot to Combat Misinformation: Exploring Gratifications, Chatbot Satisfaction and Engagement, and Relationship Quality

Grounded in the framework of uses and gratifications theory, this study investigates the impact of gratifications derived from utilizing Florence, a multilingual chatbot service developed by the World Health Organization (WHO) to disseminate COVID-19-related information. Utilizing data from a national survey comprising 591 participants in the United States, our findings reveal the significant influence of sought-after gratifications, including modality (coolness), agency (enhancement), interaction (activity), and navigability (browsing), on users’ satisfaction with the chatbot. Additionally, our research highlights the inhibiting effect of privacy risks on user satisfaction. Furthermore, we uncover a consequential relationship between chatbot satisfaction, user engagement, and the perceived relationship with the WHO, underscoring the importance of chatbot interaction in fostering favorable organization-public relationships. Empirical and practical implications of these findings are thoroughly discussed, offering valuable insights for scholars and practitioners alike.

Exploring Financial Risks and Risk Mitigation Strategies in Digital Currencies

This paper comprehensively analyzes the financial risks faced by digital currency, using digital renminbi as a case study. The risks include operational risks, privacy risks, legal and regulatory risks, currency sovereignty risks, and the impact on traditional financial systems. Corresponding risk mitigation strategies are proposed. By strengthening system architecture, employing advanced encryption technologies, enhancing legal and regulatory frameworks, maintaining policy stability, and promoting financial integration, these measures effectively mitigate financial risks associated with digital currency, fostering its sustainable development and innovation.

Barriers to Older Adults Adapting Smart Homes: Perceived Risk Scale Development

The 21st century has marked the dawn of an aging population. China’s aging process ranks first worldwide. The country has recognized the gravity of this demographic shift and implemented strategies to address it at the national level. A vast majority of elderly Chinese individuals (approximately 90%) aspire to age in their own homes. Smart homes, endowed with cutting-edge digital technologies, such as AI, the Internet of Things, and big data, hold vast potential for enabling this vision. However, acceptance of smart home products and services among elderly individuals in China remains low. The main reason is that the related products and services fail to effectively alleviate the perceived risk of this population in the R&amp;D process of related products and services, and there is a lack of effective measurement methods. To holistically assess the potential obstacles faced by elderly individuals using smart home products and services, this study targeted individuals aged 45–60 years in China. This study aimed to develop a comprehensive perceived risk scale specific to smart homes for this demographic. Initially, this study identified key risk dimensions and corresponding measurement items through a rigorous literature review, user interviews, and expert consultations. Subsequently, it ensured the reliability and validity of each dimension and its corresponding observation variables through preliminary research, exploratory factor analysis, and confirmatory factor analysis. This approach allowed for a comprehensive understanding of the challenges faced by future elderly individuals when utilizing smart home products and services, thus enabling the development of more effective solutions. The scale encompassed ten factors and seventy measurement items, including Privacy and Security Risk (seven items), Physical Risk (seven items), Technological Risk (nine items), Performance Risk (seven items), Service Risk (nine items), Financial Risk (five items), Psychological Risk (seven items), Industry and Market Risk (six items), Social Support Risk (six items), and Policy and Legal risk (seven items). The measurement scale developed in this study represents a groundbreaking first attempt to create a systematic scale for assessing the perceived risks associated with smart homes for the elderly in China. It not only enables professionals, businesses, and manufacturers to avoid or reduce barriers in the R&amp;D process of related products and services, facilitating smart home industry growth and enhancing user adoption, but also serves as a universal reference for the potential obstacles that digital technology may encounter in addressing aging-related issues, which has significant theoretical value and practical importance.

Person-centred data sharing: Empirical studies in private individuals’ attitudes

Background Recognising the power of data analytics, researchers are anxious to gain access to personal data either directly from data subjects or via research data sets. This requires a secure environment, such as a trusted research environment (TRE). However, it is unclear how the data subjects themselves regard sharing their data with TREs, especially if research goals are difficult to specify upfront or data are used for secondary purposes, making informed consent difficult to manage. We review three empirical studies to throw some light on individual attitudes to sharing health data. Methods Three anonymous, online surveys were run. The first involving 800 UK residents aimed at understanding how participants view the health data security. The second involving 500 UK residents aimed at identifying private individual views on privacy. These two surveys used a crowdsourcing platform. The third involved 1086 students at a UK university reporting their engagement with a trial diagnostic method for SARS-CoV-2. Results The first survey demonstrated that private individuals could make security decisions though they usually assume the recipient of their personal data to be responsible for all aspects of keeping the data safe. The second highlighted that individuals were aware of privacy risks but are motivated to share their data based on different contextual assumptions. The third, involving the incidental sharing of sensitive data during the SARS-CoV-2 pilot highlighted that prosocial motivations override potential personal benefit of such testing. Conclusions The three, unconnected surveys make clear that there are tensions between private individual understanding of data security and privacy risk, on the one hand, and how they behave, on the other. Respondents rely on data stewards to keep their data safe, though are likely to share even sensitive data for prosocial benefit. These findings have implications for those offering TRE services for research.

Security intrusion monitoring model for Internet of Things (IoT) using sniffing tools on wireless sensor networks

The Internet of Things (IoT) has revolutionized the way devices interact and share data over wireless sensor networks (WSN), enabling seamless connectivity and automation. However, the proliferation of IoT devices has raised serious security and privacy risks concerns due to their inherent vulnerabilities. This paper proposes a model for security intrusion monitoring by analyzing the existing literature and providing insights into the design, implementation, and effective deployment of the proposed model to detect intrusion in IoT using sniffing tools for network traffic analysis in real-time within WSN. The model passively monitors network traffic and identifies anomalous patterns, unauthorized access attempts, and abnormal device behavior. The review findings highlight the significance of the proposed model in enhancing the security of IoT systems. By detecting anomalous behavior and potential security breaches. The model enables timely response and mitigation actions to ensure the confidentiality, integrity and availability (CIA) of IoT devices data. The model includes consideration of network architecture, deployment of intrusion detection algorithms, and establishment of response mechanisms. It identifies various types of security threats, such as unauthorized access attempts, Denial-of-service, Distributed DoS, Brute-force, Heartbleed, Botnet, Inside Infiltration and device tampering, thereby providing response mechanisms that include generating alerts, isolating compromised devices, or blocking suspicious network traffic. The model incorporates a feedback loop to continuously update the detection mechanisms and adapt to evolving security threats in real-time. Series of experiments and simulations to be conducted using various IoT devices and network scenarios to evaluate model effectiveness. The model to comprise of wireless Router, MatLab for Deep Neural Network (DNN) training, Raspberry Pi, Wireshark setup and an array of Internet of Things (IoT) devices. The researcher to use dataset by extracting intrinsic, host-based and time-based attributes from Wireshark Sniffing tool. The datasets generated shall be piped by tshark to an output text file saved as a csv. Under-sampling technique to be used to address class imbalance of datasets. The model shall then be trained using the dataset to be able to detect intrusion in IoTs. The results is expected to demonstrate the model's ability to detect a wide range of security intrusions with high accuracy and minimal false positives. In conclusion, the model offers a proactive approach to safeguard IoT deployment. By leveraging sniffing tools and advanced analysis techniques, the model enhances the detection and response capabilities, enabling efficient protection against emerging threats in IoT. However, challenges associated with the model are identified, including the complexity of network monitoring and potential privacy concerns.

Role of Algorithm Awareness in Privacy Decision-Making Process: A Dual Calculus Lens

In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers’ information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, this study investigated consumers’ personal information disclosures when using an e-commerce platform with personalized algorithms. By integrating the dual calculus model and the theory of planned behavior (TPB), we constructed a privacy decision-making model for consumers. Sample data from 581 online-shopping consumers were collected by a questionnaire survey, and SmartPLS 4.0 software was used to conduct a structural equation path analysis and a mediating effects test on the sample data. The findings suggest that AA is a potential antecedent to the privacy decision-making process through which consumers seek to evaluate privacy risks and make self-disclosure decisions. The privacy decision process goes through two interrelated trade-offs—that threat appraisals and coping appraisals weigh each other to determine the (net) perceived risk and, then, the (net) perceived risk and the perceived benefit weigh each other to decide privacy attitudes. By applying the TPB to the model, the findings further show that privacy attitudes and subjective norms jointly affect information-disclosure intention whereas perceived behavioral control has no significant impact on information-disclosure intention. The results of this study give actionable insights into how to utilize the privacy decision-making process to promote algorithm adoption and decisions regarding information disclosure, serving as a point of reference for the development of a human-centered algorithm based on AA in reference to FEAT.

Communicating the Privacy-Utility Trade-off: Supporting Informed Data Donation with Privacy Decision Interfaces for Differential Privacy

Data collections, such as those from citizen science projects, can provide valuable scientific insights or help the public to make decisions based on real demand. At the same time, the collected data might cause privacy risks for their volunteers, for example, by revealing sensitive information. Similar but less apparent trade-offs exist for data collected while using social media or other internet-based services. One approach to addressing these privacy risks might be to anonymize the data, for example, by using Differential Privacy (DP). DP allows for tuning and, consequently, communicating the trade-off between the data contributors' privacy and the resulting data utility for insights. However, there is little research that explores how to communicate the existing trade-off to users. % We contribute to closing this research gap by designing interactive elements and visualizations that specifically support people's understanding of this privacy-utility trade-off. We evaluated our user interfaces in a user study (N=378). Our results show that a combination of graphical risk visualization and interactive risk exploration best supports the informed decision, \ie the privacy decision is consistent with users' privacy concerns. Additionally, we found that personal attributes, such as numeracy, and the need for cognition, significantly influence the decision behavior and the privacy usability of privacy decision interfaces. In our recommendations, we encourage data collectors, such as citizen science project coordinators, to communicate existing privacy risks to their volunteers since such communication does not impact donation rates. %Understanding such privacy risks can also be part of typical training efforts in citizen science projects. %DP allows volunteers to balance their privacy concerns with their wish to contribute to the project. From a design perspective, we emphasize the complexity of the decision situation and the resulting need to design with usability for all population groups in mind. % We hope that our study will inspire further research from the human-computer interaction community that will unlock the full potential of DP for a broad audience and ultimately contribute to a societal understanding of acceptable privacy losses in specific data contexts.

Collective Privacy Sensemaking on Social Media about Period and Fertility Tracking post Roe v. Wade

On June 24, 2022, the U.S. Supreme Court overturned Roe v. Wade, which has led to full bans on most abortions in 14 states within one year. Many people in the U.S. use period and fertility tracking apps for reproductive healthcare and concerns have arisen about the privacy risks these apps might pose in the wake of Roe reversal. Existing literature on privacy risks of period and fertility tracking apps has primarily examined the privacy policies and practices of these apps. However, how users make sense of the privacy risks of these apps, especially in the post-Roe time, remains understudied. This study explores collective privacy sensemaking on social media, a practice in which people collectively make sense of a privacy situation. Our findings reveal how people contextualize privacy issues, speculate about the associated risks, as well as explore risk mitigation strategies. We conclude with privacy design implications for privacy design in period and fertility tracking apps and contribute insights that could inform policymaking and legal perspectives.

Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use

AI assistants such as Alexa, Google Assistant, and Siri, are making their way into the healthcare sector, offering a convenient way for users to access different healthcare services. Trust is a vital factor in the uptake of healthcare services, but the factors affecting trust in voice assistants used for healthcare are under-explored and this specialist domain introduces additional requirements. This study explores the effects of different functional, personal, and risk factors on trust in and adoption of healthcare voice AI assistants (HVAs), generating a partial least squares structural model from a survey of 300 voice assistant users. Our results indicate that trust in HVAs can be significantly explained by functional factors (usefulness, content credibility, quality of service relative to a healthcare professional), together with security, and privacy risks and personal stance in technology. We also discuss differences in terms of trust between HVAs and general-purpose voice assistants as well as implications that are unique to HVAs.

Evaluating the Use of Hypothetical 'Would You Rather' Scenarios to Discuss Privacy and Security Concepts with Children

Children are exposed to technology at home and school at very young ages, often using family mobile devices and educational apps. It is therefore critical that they begin learning about privacy and security concepts during their elementary school years, rather than waiting until they are older. Such skills will help children navigate an increasingly connected world and develop agency over their personal data, online interactions, and online security. In this paper, we explore how a simple technique---a ''Would Your Rather'' (WYR) game involving hypothetical privacy and security scenarios---can support children in working through the nuances of these types of situations and how educators can leverage this approach to support children's privacy and security learning. We conducted three focus groups with 21 children aged 7-12 using the WYR activity and interviewed 13 elementary school teachers about the use of WYR for facilitating privacy and security learning. We found that WYR provided a meaningful opportunity for children to assess privacy and security risks, consider some of the social and emotional aspects of privacy and security dilemmas, and assert their agency in a manner typically unavailable to children in an adult-centric society. Teachers highlighted connections between privacy and security dilemmas and children's social and emotional learning and offered additional insights about using this WYR technique in and beyond their classrooms. Based on these findings, we highlight four opportunities for using WYR to support children in engaging with privacy and security concepts from an early age.

On Being an Expert: Habitus as a Lens for Understanding Privacy Expertise

How do privacy experts and lay persons differ? We investigate this question using data about the use of privacy-enhancing technologies and strategies from 128 surveys and 17 follow-up interviews with two populations: privacy experts (i.e., privacy researchers and professionals) and privacy laypersons. Findings reveal that both experts and laypersons use common privacy strategies, but experts employ a broader range of strategies, favor open source technologies, and possess a more technical understanding of internet privacy risks and technologies such as onion routing and tracking algorithms. We characterize these differences in terms of sociological habitus, where experts and laypersons differ not only in technical skill but also in broader conceptualizations and practices. From this characterization, we make recommendations for technology design practices, as well as legal and pedagogical implications, that can help decenter the experiences of privacy experts and accommodate privacy laypersons' preferences and habits.

Consumers’ perceptions of the environmental and public health benefits of last mile drone delivery

ABSTRACT Using Technology Acceptance Model, Theory of Planned Behavior, and Privacy Calculus as theoretical frameworks, this study examined environmental and public health/safety dispositions and environmental and public health/safety benefits, perceived usefulness, privacy risk, and trust as they relate to U.S consumers intention to adopt drone delivery services. This study employed Partial Least Squares – Structural Equation Modeling methods and data were collected online using Amazon Mechanical Turk, resulting in 687 respondents. The results show that the intention to use drones for delivery increases if consumers perceive drone delivery to be useful. Additionally, perceived usefulness was influenced by a person’s environmental and public health/safety dispositions along with related benefits. Additionally, as a person’s perceived usefulness increases their perceived privacy risk decreases. Further, this study found that organisational trust moderates the negative effect of privacy risk on drone adoption.

Customers’ intention to use virtual reality in tourism: a comprehensive analysis of influencing factors

Purpose The emergence of virtual reality (VR) has the potential to revolutionize various industries, including tourism, as it delivers a simulated environment that closely emulates real-life experiences. Therefore, this study aims to explore how the factors, i.e. enjoyment, emotional involvement, flow state, perceived privacy risk, physical risk and cost, influence the customers’ intention to use VR for tourism. Design/methodology/approach This study integrates the technology acceptance model, hedonic consumption theory with other factors, including cognitive response, authenticity, perceived privacy risk, perceived physical risk, perceived cost and perceived presence. Partial least squares structural equation modelling approach was used to test the proposed research model. Findings The finding based on the sample of 252 respondents revealed that authenticity is the most influential factor impacting behavior intention followed by perceived cost, attitude, cognitive response and enjoyment. Also, the study supported the moderating impact of personal innovativeness between attitude and behavioral intention to use VR for tourism. Practical implications The findings of the study offers practical implications for service providers, site managers, destination marketers, tourist organizations and policymaker to develop more effective strategies for offering VR services for tourism. Originality/value This study enriches the current understanding of VR adoption in context of tourism with empirical evidences.