Privacy Control Research Articles

The effectiveness of e-procurement systems in Zambia: A case study of Ministry of Health Lusaka province

E-procurement is a dynamic process that involves the seamless integration of various online activities, such as order creation, supplier selection, contract signing, and product delivery, all facilitated by the Internet. Governments worldwide are increasingly emphasizing the importance of effective electronic procurement systems in promoting transparency, accountability, and efficiency in public procurement processes, where taxpayer funds are utilized. In Zambia, despite significant efforts by the government to promote the adoption of electronic procurement systems, such as e-procurement, challenges persist in ensuring their effectiveness. The failure to address the effectiveness of e-procurement within the Ministry of Health could have severe consequences. Inadequate adherence to procurement laws and regulations, as evidenced by the ZPPA report, may lead to inefficiencies, delays in procurement processes, and mis-allocation of resources. These inefficiencies can ultimately compromise the quality and availability of healthcare services, negatively impacting patient outcomes and public health. Additionally, the inability to harness the potential benefits of e-procurement, such as cost savings and streamlined processes, may perpetuate resource wastage and hinder efforts to improve healthcare delivery in Zambia. The main objective of this study was to assess the effectiveness of E-procurement systems at the Ministry of Health. The research philosophy for this study adopted a Quantitative approach that emphasized the practical utility of research methods in addressing the research problem. The findings of the study showed that the evaluation of the e-procurement system within the Ministry yielded a comprehensive understanding of its current performance and highlighted areas needing improvement. The e-procurement system has generally been well-received by the majority of respondents, reflecting its positive impact on procurement processes. Users have rated the system as effective in streamlining operations, enhancing procurement efficiency, and being user-friendly. Based on the comprehensive analysis and findings of the study, the following recommendations were proposed to enhance the effectiveness of the e-procurement system within the Ministry: Enhance Communication, Boost Supplier Participation, Improve Supplier Responsiveness, Strengthen Data Security and Privacy Controls and Increase Transparency. By addressing these recommendations, the Ministry can significantly improve the effectiveness of its e-procurement system, leading to more efficient procurement processes, greater satisfaction among stakeholders, and a more streamlined and secure procurement environment. In practice, the study offered actionable insights for healthcare practitioners and industry professionals involved in supply chain management. From a policy perspective, the study informed the development of evidence-based policies within the Ministry of Health and other relevant governmental bodies

PRIVATE LINK TRANSFER HUB: A SECURE PROTOCOL FOR PRIVATE FILE TRANSFERS

This paper introduces a secure file and message transferring website, Private link transfer, designed to prioritize extreme privacy and user control. The platform enables users to send files through One- Time Temporary Links, which are both expirable and highly configurable. These links can be tailored with a range of limitations, including access duration, IP restrictions, password protection, authentication keys, custom encryption, and a defined number of allowable views. Such features ensure a high level of security and privacy, minimizing unauthorized access or prolonged data exposure. In addition to these privacy-focused controls, Private Link Transfer offers a seamless user experience by incorporating seven premium themes, catering to diverse user preferences. The underlying system leverages a peer-to-peer file transfer protocol, eliminating the need for third-party intermediaries such as servers or cloud storage providers. By utilizing cutting-edge web technologies, this protocol establishes a direct connection between devices, ensuring faster, more efficient, and secure file transfers. The methodology combines advanced encryption with stringent limitations, protecting sensitive data against unauthorized access and mitigating the risks of interception during transmission. This paper explores the development and implementation of the protocol, detailing its key features and advantages over traditional file transfer methods. The proposed system is particularly suited for scenarios requiring temporary data sharing with strict privacy controls. It addresses modern challenges in secure file transfer, offering an innovative solution that aligns with the growing demand for enhanced digital privacy and security. Keywords: Secure File Transfer, Temporary Expirable Links, Peer-to-Peer (P2P) Protocol, End-to-End Encryption, Data Privacy, Access Control, Custom Encryption.

Optimized Multi-Level Security for Content Contribution and Retrieval in Online Social Networks using a Content Visualization Mechanism

Online social networks have become an integral part of modern communication, providing platforms for users to share personal information, media, and opinions. However, these platforms face significant challenges in preserving user privacy while ensuring efficient data retrieval and maintaining data integrity. Existing privacy preservation methods, such as PPK-MEANS, CFCAF, and CLDPP, are limited in their ability to handle the growing complexity and scale of user data, often leading to inefficiencies such as high Content Retrieval Time (CRT), increased Information Loss (IL), and compromised data accuracy. These inefficiencies are crucial to address, as they can degrade the user experience by causing delays, compromising data integrity, and limiting system scalability. High CRT frustrates users, while increased IL reduces data accuracy, undermining trust and system reliability. The primary issue addressed in this study is the need for an advanced privacy-preserving mechanism that can provide multilevel security while maintaining optimal system performance. To overcome these limitations, the Layered Secure Online Collaborative Verification (LSOCV) algorithm is proposed, designed to offer a scalable solution with tiered privacy controls based on user requirements. LSOCV enhances Privacy Retrieval Accuracy (PRA), significantly reduces CRT, and minimizes IL. The experimental results show that LSOCV achieved a PRA of 91.97%, reduced CRT to 7ms, and decreased IL by up to 8% for 500KB files, outperforming existing approaches. This method provides robust privacy protection and efficient data handling on social networks, with the potential for future application in big data environments, such as Hadoop, to ensure scalable, secure, and efficient privacy-preserving solutions.

Artificial Intelligent Branching Simulations (AIBS) in Critical Care Neonatal Nursing

Roleplaying, simulations and branching scenarios are among the most authentic, critical and effective educational experiences, though have seen limited adoption due to the expense of creating, staffing, adapting and sustaining them over time (Davies, 2013). The new generation of large language models have made possible new affordances that enable delivering scenario based roleplaying experiences more sustainably. This Pecha Kucha charts the development process, frontier technology and the positive outcomes of an artificial, intelligent branching simulation (AIBS) a in critical care neonatal nursing simulation. Critical care disciplines and nursing have a tradition of using expensive in-person simulations and labor-intensive, high-stress scenarios to prepare students for the workplace (Jefferies, 2020). After such training, nurses undergo rigorous, high pressure in-person evaluations, such as the ANSAT (Ossenberg, Mitchell & Henderson, 2020), which they must pass in order to practice. Foundational reading about nursing practices and reviewing practice manuals is a step towards passing such evaluations, but these more theoretical learning practices don’t constructively align with the situational, authentic skills that are demanded in practice. With few learning experiences that can scaffold towards critical in-person evaluation, students often report high levels of anxiety and that they feel inadequately prepared for in-person evaluation, sometimes failing and leaving their studies and aspirations entirely (Cornine, 2020). Mindful also of an industry wide shortage of critical care nurses, there is a clear need to find bridging solutions that can affordably and effectively prepare trainee nurses for demanding in-person workplace evaluations. This Pecha Kucha charts the development of AIBS that was designed to bridge a learning gap between more theoretical training methods and high stress in-person training evaluations. The increasing popularity of conversational agents powered by large language models on web-based platforms like Character.ai (Sarkar, 2024), suggests that students might find a chatbot an approachable, engaging and an increasingly familiar experience. Initial tests directly prompting ChatGPT and Claude.ai both showed promise, but also raised some issues around reliability which mandated more direct and qualified oversight of teaching staff. A technical solution was required as an intermediary between the students and the LLMs that enabled ‘human-in-the-middle’ (Mollick, 2024) oversight. After an extensive review, the educator created tool ‘Cogniti’ was selected due to it’s ability to provide a seamless, pretrained scenario chat experience, while also enabling student account integration, data governance, supervisor oversight and privacy controls. The prompts used to instruct the Cogniti AI agent were developed using the RTRI prompting framework (Lui 2023, Hardman, 2023). By separating parts of the scenario into sequential chunks, each with a series of ‘expected responses’ it became possible to create and align an improvisational prompting script that resembled a sequence from a typical hospital procedures manual and that aligned to the prescribed ANSAT placement evaluation framework (Ossenberg, Mitchell & Henderson, 2020). Allocating specific roles to the AI agent and to the trainee nurse within a framework of expected responses enabled the narrative to resemble an authentic interaction, but within a safe environment. Prompting the AI agent to progress the narrative under specific conditions and to provide structured feedback to students, enabled a cohesive educational experience that was able to address specific gaps in student knowledge of neonatal practice and procedure. Staff and student feedback demonstrated positive results in terms of engagement, satisfaction, academic performance and student well-being.

The Future of Smart Home Security: Generative AI and LLMs for Intelligent Event Detection and Personalized Notifications

Abstract—Smart home security cameras are becoming more common, but their usefulness can be diminished by notification fatigue from too many alerts about minor incidents. This paper examines the gaps of existing event detection and notification systems in security cameras and then recommends using Generative AI and Large Language Models (LLMs) to add intelligence which would improve user experience. Generative AI can be leveraged to classify events more accurately and assist with anomaly detection. LLMs can further be used to create notifications that are tailored to the context and personalized to users behavior, helping to reduce notification fatigue and provide meaningful user alerts. The paper also looks into wider applications of these technologies to add intelligence and improve other related experiences like automated video summarization, proactive security measures, and improved privacy controls. The integration of Generative AI and LLMs with smart home security camera systems advances the smart cameras capabilities and offers enhanced security, personalized user experiences. Keywords—Smart home security, Generative AI, Large Language Models (LLMs), Event detection, Anomaly detection, Notification fatigue, Context-aware notifications, Personalized security, Reinforcement Learning from Human Feedback (RLHF), Internet of Things (IoT).

Privacy for Groups Online: Context Matters

The pervasive influence of online activities in our lives, encompassing personal connections, professional engagements, and e-commerce, has amplified concerns about privacy. However, existing privacy research has predominantly concentrated on the individual level, paying less attention to the privacy practices and strategies adopted by online social groups. This research gap calls for a renewed focus on understanding and addressing privacy challenges specific to online group settings. In this paper we explore the privacy needs of online groups through the lens of Contextual Integrity. We perform two complementary studies: semi-structured qualitative interviews of Facebook Groups users (n=17), and a large-scale survey of individuals organizing in groups on Facebook, Discord, and Reddit (n=4486). We investigate the privacy needs of different contextual groups, and locate the presence of contextual norms, contextual member roles, explicit and implicit rules, and privacy concerns. We trace how this complex interplay informs privacy expectations, needs, and negotiations across groups. We find that technical systems provide limited tools to effectively enforce group privacy, allowing individuals to compromise privacy norms. Based on these findings, we offer recommendations to support the design of privacy controls for online groups.

Explaining Privacy Paradox on WeChat

With the development of the information age, people need to disclose personal information in exchange for socializing and related services. At the same time, people's privacy concerns are gradually increasing. Privacy paradox has become a normal phenomenon in social network sites (SNS). The loss of control of privacy might lead to privacy fatigue. Previous work has studied the mechanism of privacy paradox and explored the influence of specific factors on privacy paradox. However, the effect of privacy fatigue on privacy paradox among SNS users has not been studied. In this study, we use structural equation modeling (SEM) to explore the effect of privacy fatigue on personal information disclosure intentions and actual behaviors among WeChat users. Our results of 710 participants showed that privacy fatigue has a mediating role between privacy concern and privacy disclosure intention, and has a positive influence on privacy disclosure intention and disclosure behavior. Based on these results, we provide implications to reduce privacy fatigue and privacy paradox.

Abstract B092: ‘We’ve got to go to the doctor and we’re scared to death’: Community members perspectives on sexual orientation and gender identity data collection in healthcare settings

Abstract Purpose: Much research is needed to advance our knowledge of LGBTQ cancer disparities, but research is hindered by inconsistency in collection of data on sexual orientation and gender identity (SOGI) across clinical and research settings. Nationally, there have been increased efforts to standardize SOGI data collection. The purpose of this study was to identify potential patient-level barriers and facilitators to SOGI data collection within the catchment area of a midwestern cancer center. Methods: Investigators conducted 4 focus groups in partnership with community-based organizations and stakeholder groups that reflected the catchment area’s diverse populations. Using a semi-structured approach, participants were asked about their experiences with SOGI data collection, barriers and facilitators to this collection, and how they viewed the practice of collecting SOGI data. Focus groups were audio recorded and transcribed. Data was coded and interpreted using applied thematic analysis. Results: Four focus groups were conducted with 32 participants in total. There were 2 LGBTQ groups (one mixed race/ethnicity, one African American), and 2 predominantly non-LGBTQ groups (one African American and one Middle Eastern/North African or MENA). Across all participants, 53% were African American (n=17), 25% identified as MENA (n=8), 28% were White (n=9), 44% of the participants identified as women (n=19), one individual identified as a transgender woman & 2 individuals as non-binary, and 53% reported their sexual orientation as gay/lesbian/same-loving. In all groups, participants emphasized a need to educate both patients and providers about the importance of SOGI data collection, why it is collected, and its relevance to improving medical care. Participants further discussed the quality and depth of the patient-provider relationship as a critical factor in a patient’s comfort and willingness to share SOGI data. Participants also underscored the importance of privacy ownership (i.e., the ownership of one’s own private information) and control in relation to SOGI status, and the desire to minimize potential privacy turbulence of an unexpected and unwelcome disclosure breach. All groups discussed how the potential for harm resulting from SOGI disclosure could deter patients from sharing SOGI information. Those who identified as LGBTQ, recounted both personal experiences and the experiences of others in which disclosure of SOGI information directly led to poor treatment by a healthcare professional. It is this potential for harm and discrimination that led many participants to stress the need for privacy control until a safe environment is established. Conclusions: Findings highlight a number of potential patient barriers to SOGI data collection from community members’ perspectives, including the need for widespread education on this topic and clinical environments in which patients are safe disclosing SOGI status. Future research and practice should consider specific methods to normalize the practice of SOGI data collection in the cancer care context. Citation Format: Ariel Washington, Ali Fakih, Lindsey Toman, Riham Ayoub, Emma Noble, Susan Musto, Brittany Dowe, Scott Ellis, Andrea Sakleh, Hayley S. Thompson, Latonya Riddle-Jones. ‘We’ve got to go to the doctor and we’re scared to death’: Community members perspectives on sexual orientation and gender identity data collection in healthcare settings [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B092.

Acceptance and self-protection in government, commercial, and interpersonal surveillance contexts: An exploratory study

Digital surveillance is pervasive in cyberspace, with various parties continuously monitoring online activities. The ways in which internet users perceive and respond to such surveillance across overlapping contexts warrants deeper exploration. This study delves into the acceptance of digital surveillance by internet users and their subsequent self-protective actions against it in three distinct contexts: government, commercial, and interpersonal surveillance. Survey responses collected from 356 internet users in the U.S. showed that acceptance levels for surveillance varied between institutional and interpersonal contexts. However, the degree of self-protection remained consistent across all three contexts. Privacy concerns, algorithm awareness, and perceived privacy control played nuanced roles to both surveillance acceptance and self-protection measures in each context. Interestingly, political orientation emerged as a significant moderating factor on surveillance acceptance. Conservative-leaning participants were less accepting of surveillance overall, especially government surveillance. For conservatives, higher privacy concerns meant less acceptance of both government and corporate surveillance. Liberals’ acceptance levels remained fairly consistent and were less affected by privacy concerns. These findings underscore the significance of contextual differences in privacy and surveillance research and provide implications for refining the existing theoretical frameworks.

Exploring the Impact of Social Robot Design Characteristics on Users’ Privacy Concerns: Evidence from PLS-SEM and FsQCA

Although an increasing number of studies explore the factors influencing users’ privacy concerns regarding social robots, the existing understanding of this issue remains largely fragmented. Previous studies have mainly focused on the "net effect" between variables, leaving the complexity of causal configurations, and the holistic impact of design characteristics of social robots on user privacy concerns remains unclear. Based on the Stimuli-Organism-Response (S-O-R) framework and Communication Privacy Management Theory (CPMT), this study integrates social robot design characteristics such as Anthropomorphism, Warmth, Competence, and Transparency into causal configurations, and uses Perceived Privacy Risk and Perceived Privacy Control as mediating variables to propose a Comprehensive conceptual model. Based on valid data from a sample of 198 Chinese social robot users, this study conducted empirical analyses of the conceptual model using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (FsQCA). PLS-SEM results show that anthropomorphism, warmth, competence, and transparency are key factors influencing privacy concerns, and perceived privacy risk mediates the relationship between warmth, information transparency, and privacy concerns. The FsQCA results further validated the findings of PLS-SEM and identified five configurations of factor combinations that led to higher levels of user privacy concerns. Among them, the combination of high anthropomorphism design, high competence, and low warmth of social robots is the core configuration that leads to users’ privacy concerns. Overall, this study broadens our understanding of social robot users’ privacy concerns and reveals the causal complexity behind social robot users’ privacy concerns. It provides some theoretical and practical insights for subsequent scholars and designers.

The IT‐driven ridesharing economy at the base of the pyramid: Unravelling the impact of uncertainty reduction on drivers' engagement in ridesharing

AbstractDespite the potential benefits for drivers at the base of the pyramid (BoP) of IT‐driven ridesharing businesses, drivers are susceptible to uncertainty owing to their low education and incomes, potentially compelling them to engage less in ridesharing over time. Although this important sociotechnical phenomenon has garnered increasing attention, IS studies on this topic are scarce. Drawing on uncertainty reduction theory, we explored mechanisms for reducing BoP drivers' uncertainty and building their trust to enable them to engage in ridesharing. We identified three means of uncertainty reduction at three levels: trust in passengers, trust in ridesharing platforms, and the effectiveness of ridesharing institutional mechanisms. Using a sequential, triangulated mixed‐methods approach, we conducted 30 semi‐structured interviews to explore trust building with drivers and used data provided by 303 ridesharing drivers to quantitatively test the proposed research model. We found that perceived empathy and information congruity increased BoP drivers' trust in passengers, whereas effective escrow services, perceived justice, matching accuracy, and a high level of privacy control increased BoP drivers' perceived trust in ridesharing platforms, motivating them to engage more frequently with ridesharing platforms. Furthermore, the results indicated that increasing trust in both passengers and ridesharing platforms and improving the effectiveness of ridesharing institutional mechanisms can enhance BoP drivers' engagement in ridesharing. We conducted 30 follow‐up interviews with BoP drivers to corroborate the quantitative results and draw robust inferences.

When an AI Doctor Gets Personal: The Effects of Social and Medical Individuation in Encounters With Human and AI Doctors

How do we know when someone knows us? Does it matter whether the knower is a human or a machine? Following the theory of interpersonal knowledge, a between-subjects experiment investigated whether a doctor’s incorporation of individualized knowledge about a patient’s social or medical history enhances doctor-patient relationships in online conversations. Patients in this study conversed with either a human doctor, an AI doctor, or an AI-assisted human doctor. Following previous research, additional factors such as perceptions of effort, relational closeness, privacy intrusiveness, and the provision of privacy control were assessed. Results showed that an AI doctor enhanced patient satisfaction when it employed social individuation messages, which triggered perceptions of increased effort, but only when patients could activate privacy control. Perception of relational closeness with a human doctor and an AI-assisted human doctor did not seem to require social individuation and privacy control. The study concludes with implications for the theory of interpersonal knowledge and AI-mediated communication research, as well as practical implications for improving chatbot medical systems.

Elicitation of security threats and vulnerabilities in Insurance chatbots using STRIDE

Although chatbots are used a lot for customer relationship management (CRM), there needs to be more data security and privacy control strategies in chatbots, which has become a security concern for financial services institutions. Chatbots gain access to large amounts of vital company information and clients’ personal information, which makes them a target of security attacks. The loss of data stored in chatbots can cause major harm to companies and customers. In this study, STRIDE (viz. Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege) modelling was applied to identify the data security vulnerabilities and threats that pertain to chatbots used in the insurance industry. To do this, we conducted a case study of a South African insurance organisation. The adopted methodology involved data collection from stakeholders in the insurance organisation to identify chatbot use cases and understand chatbot operations. After that, we conducted a STRIDE-based analysis of the chatbot use cases to elicit security threats and vulnerabilities in the insurance chatbots in the organisation. The results reveal that security vulnerabilities associated with Spoofing, Denial of Service, and Elevation of privilege are more relevant to insurance chatbots. The most security threats stem from Tampering, Elevation of privilege, and Spoofing. The study extends the discussion on chatbot security. It fosters an understanding of security threats and vulnerabilities that pertain to insurance chatbots, which is beneficial for security researchers and practitioners working on the security of chatbots and the insurance industry.

Integrasi Artificial Intelegent Berbasis Sistem Operasi Android pada Smart Home

This study explores the integration of Artificial Intelligence (AI) into smart home systems using the Android operating system to enhance security, privacy, efficiency, and user comfort. Key security measures include data encryption, robust authentication methods, sandboxing, and AI integration, specifically leveraging Google Assistant for improved privacy controls. Maintenance strategies for smart homes emphasize energy management, device condition monitoring, and enhanced safety features. AI adaptation to user habits enhances productivity and situational awareness, while Android's role in connecting various IoT devices facilitates remote control and energy-efficient recommendations. Methods such as Eco Android, Greensource, byte-code transformations, and automated energy diagnosis tools aid in optimizing energy use. The comparison between smart and non-smart homes highlights the efficiency and convenience of smart homes despite higher installation costs and potential network issues. The development and deployment of an Android-based application, SafeHause, exemplifies practical implementation, emphasizing end-to-end testing, security updates, and user education. The findings affirm that AI integration with Android significantly improves the smart home experience by enhancing energy optimization, data security, and personalized user interaction. Furthermore, the study discusses future trends in smart home technology, such as the potential for more advanced AI algorithms and machine learning techniques to provide even greater personalization and automation. The importance of regular software updates and the role of user feedback in refining smart home systems are also highlighted, ensuring that these technologies continue to evolve and meet user needs effectively.

IoT Privacy Risks Revealed.

A user's devices such as their phone and computer are constantly bombarded by IoT devices and associated applications seeking connection to the user's devices. These IoT devices may or may not seek explicit user consent, thus leaving the users completely unaware the IoT device is collecting, using, and/or sharing their personal data or, only marginal informed, if the user consented to the connecting IoT device but did not read the associated privacy policies. Privacy policies are intended to inform users of what personally identifiable information (PII) data will be collected about them and the policies about how those PII data will be used and shared. This paper presents novel tools and the underlying algorithms employed by the Personal Privacy Assistant app (UTCID PPA) developed by the University of Texas at Austin Center for Identity to inform users of IoT devices seeking to connect to their devices and to notify those users of potential privacy risks posed by the respective IoT device. The assessment of these privacy risks must deal with the uncertainty associated with sharing the user's personal data. If privacy risk (R) equals the consequences (C) of an incident (i.e., personal data exposure) multiplied by the probability (P) of those consequences occurring (C × P), then efforts to control risks must seek to reduce the possible consequences of an incident as well as reduce the uncertainty of the incident and its consequences occurring. This research classifies risk according to two parameters: expected value of the incident's consequences and uncertainty (entropy) of those consequences. This research calculates the entropy of the privacy incident consequences by evaluating: (1) the data sharing policies governing the IoT resource and (2) the type of personal data exposed. The data sharing policies of an IoT resource are scored by the UTCID PrivacyCheck™, which uses machine learning to read and score the IoT resource privacy policies against metrics set forth by best practices and international regulations. The UTCID Identity Ecosystem uses empirical identity theft and fraud cases to assess the entropy of privacy incident consequences involving a specific type of personal data, such as name, address, Social Security number, fingerprint, and user location. By understanding the entropy of a privacy incident posed by a given IoT resource seeking to connect to a user's device, UTCID PPA offers actionable recommendations enhancing the user's control over IoT connections, interactions, their personal data, and, ultimately, user-centric privacy control.

Seeing the forest and the trees: a meta-analysis of the antecedents to online self-disclosure

PurposeA wealth of studies have identified numerous antecedents to online self-disclosure. However, the number of competing theoretical perspectives and inconsistent findings have hampered efforts to obtain a clear understanding of what truly influences online self-disclosure. To address this gap, this study draws on the antecedent-privacy concern-outcome (APCO) framework in a one-stage meta-analytical structural equation modeling (one-stage MASEM) study to test a nomological online self-disclosure model that assesses the factors affecting online self-disclosure.Design/methodology/approachUsing the one-stage MASEM technique, this study conducts a meta-analysis of online self-disclosure literature that comprises 130 independent samples extracted from 110 articles reported by 53,024 individuals.FindingsThe results reveal that trust, privacy concern, privacy risk and privacy benefit are the important antecedents of online self-disclosure. Privacy concern can be influenced by general privacy concern, privacy experience and privacy control. Furthermore, moderator analysis indicates that technology type has moderating effects on the links between online self-disclosure and some of its drivers.Originality/valueFirst, with the guidance of the APCO framework, this study provides a comprehensive framework that connects the most relevant antecedents underlying online self-disclosure using one-stage MASEM. Second, this study identifies the contextual factors that influence the effectiveness of the antecedents of online self-disclosure.

Trust in Public and Private Providers of Health Apps and Usage Intentions

Mobile health apps, particularly personal health records (PHRs), play a vital role in healthcare digitalization. However, the varying governance approaches for providing PHR platforms have led to a growing debate on the adequate regulation of health technology with regard to their adoption. This article investigates how provider governance, whether public or private, influences users’ intentions to use and decisions to download a PHR app. Drawing on institutional trust, privacy calculus, and privacy control frameworks, the study develops hypotheses about how provider governance affects the pathways through which trust influences users’ intentions to adopt the app. Data acquired from an online experiment in the German market reveals that users exhibit a higher level of trust in public providers compared to the same app provided by private companies. Furthermore, provider governance significantly alters the paths in how trust influences usage intentions through perceived benefits, perceived risks, and privacy control. These findings contribute to the development of a sectoral theory of privacy calculus and privacy control in Information Systems (IS). Moreover, they offer practical insights for healthcare regulators and health app providers with the aim of promoting the acceptance and usage of PHRs and other mobile health apps.

High-fidelity synthetic patient data applications and privacy considerations

This paper explores the potential applications of high-fidelity synthetic patient data in the context of healthcare research, including challenges and benefits. The paper starts by defining synthetic data, types of synthetic data and approaches to generating synthetic data. It then discusses the potential applications of synthetic data in addition to as a privacy enhancing technology and current debates around whether synthetic data should be considered personal data and,therefore, should be subjected to privacy controls to minimise reidentification risks. This will be followed by a discussion of privacy preservation approaches and privacy metrics that can be applied in the context of synthetic data. The paper includes a case study based on synthetic electronic healthcare record data from the Clinical Practice Research Datalink on how privacy concerns due to reidentification have been addressed in order to make this data available for research purposes. The authors conclude that synthetic data, particularly high-fidelity synthetic patient data, has the potential to add value over and above real data for public health and that it is possible to address privacy concerns to make synthetic data available via a combination of privacy measures applied during the synthetic data generation process and post-generation reidentification risk assessments as part of data protection impact assessments.

Cation-Modified Poly(vinyl alcohol) Film to Optimize the Bistability of Transparent Electrophoretic Display Based on Charge Adsorption Behavior in Nonpolar Solvent.

Electrophoretic displays (EPDs) utilize the electrophoretic particles in electronic ink (e-ink) to display different color states with bistability. Bistability of EPDs is achieved by placing colloidal particles in a highly viscous solvent to keep the distribution of colloidal particles stable without sustaining the external field, so it only consumes power when updating the image. The feature of low power consumption makes it suitable for applications such as advertising boards, price tags, etc. Apart from these applications, recent research on lateral-driving EPDs extends its applications to smart windows, privacy control, and so on. However, achieving bistability by simply increasing the viscosity of solvent is inefficient in the case of lateral driving operation. Therefore, it is deserving to have intensive study on the mechanism of bistability from other aspects. Herein, we propose a mechanism to investigate the charge adsorption behavior on the electrode to affect the bistability of particles, which is based on the "Stern layer adsorption/desorption" model. Based on the above mechanism, we further fabricated a hexadecyl trimethylammonium bromide (CTAB)/poly(vinyl alcohol) (PVA) composite film on the electrode to improve the bistability of lateral-driving EPD by reducing the diffusion current caused by unabsorbed charges. This developed lateral-driving EPD can significantly improve the bistability, which is enhanced from 40 s to 7 min, an increase by a factor of approximately 10. This work gives a way to consider the bistability of colloidal particles in nonpolar solvent.

Human resource records management culture and privacy control in Ghana: the roles of employee experience and top management commitment

Purpose The purpose of this study was to investigate the roles of employee experience and top management commitment in the relationship between human resource (HR) records management culture and HR records privacy control in organisations in Ghana. Design/methodology/approach Structural equation modelling was used in analysing the data. Following the specification of the model, three main types of analyses were carried out. They were reflective measurement model analyses to test reliability and validity; formative measurement model analyses to test redundancy, collinearity, significance and relevance of the lower-order constructs; and structural model analyses to ascertain the explanatory and predictive powers of the model, significance of the hypotheses and their effect sizes. Findings The study confirmed that communication, privacy awareness and training and risk assessment are dimensions of HR records management culture. Concerning the hypotheses, it was established that HR records management culture is related to HR records privacy control. Also, the study showed that employee experience positively moderated the relationship HR records management culture has with HR records privacy control. However, top management commitment negatively moderated the relationship HR records management culture has with HR records privacy control. Practical implications Organisations committed to the privacy control of HR records need to ensure the retention of their employees, as the longer they stay with the organisation, the more they embody the HR records management culture which improves the privacy control of HR records. For top management commitment, it should be restricted to providing strategic direction for HR records privacy control, as the day-to-day influence of top management commitment on the HR records management culture does not improve the privacy control of HR records. Originality/value This study demonstrates that communication, privacy awareness and training and risk assessment are dimensions of HR record management culture. Also, the extent of employee experience and top management commitment required in the relationship between HR records management culture and HR records privacy control is revealed.