Article III standing is difficult to achieve in the context of data security and data privacy claims. Injury in fact must be “concrete,” “particularized,” and “actual or imminent” — all characteristics that are challenging to meet with information harms. This Article suggests looking to an unusual source for clarification on privacy and standing: recent national security surveillance litigation. There we can find significant discussions of what rises to the level of Article III injury in fact. The answers may be surprising: the interception of sensitive information; the seizure of less sensitive information and housing of it in a database for analysis; and harms arising from data analytics have all been taken seriously in recent national security cases. This Article closes by noting that no discussion of corporate responsibility and data theft can be complete without addressing the roles corporations play in challenging the national security state.

https://scholar.law.colorado.edu/cgi/viewcontent.cgi?article=1831&context=articles

Standing After Snowden: Lessons on Privacy Harm from National Security Surveillance Litigation

Human-Centered Design for Individual and Social Well- being: Editorial Preface

Employee Assistance Programs (EAPs) are commonly used to manage stress and well-being in organizations in Thailand; however, their utilization rates remain low among employees. This study investigates the factors that influence employees’ utilization of an EAP, focusing on the moderating roles of perceived privacy protection and perceived trust, using the Health Belief Model (HBM). Data were collected by surveying 434 employees from organizations providing EAPs in Thailand. Structural Equation Modelling was employed to analyze the data and test the hypotheses. The findings indicated that employees’ intention to use EAPs was significantly influenced by perceived benefits, cues to action, and privacy protection, whereas self-efficacy had a negative effect. Moreover, perceived privacy protection can strengthen the relationship between self-efficacy and EAP utilization, while it weakens the relationship between cues to action and perceived barriers. Perceived trust further enhances the impact of perceived severity and self-efficacy on EAP utilization. The practical implications for organizations are also discussed. 

Enhancing Employee Assistance Programs for Stress Management in Thailand: Employees’ Perspectives

The increasing sophistication of cyber threats has driven the evolution of network security mechanisms. Traditional methods like firewalls, IDS, and antivirus software, which rely on established guidelines and signature-based detection, are now limited in addressing new and unknown threats. AI has introduced a new paradigm in network security, utilizing machine learning, behavioral analytics, and automated threat detection. AI-driven methods offer improved efficiency, accuracy, response time, adaptability, scalability, and cost-effectiveness. They analyze large data sets to detect patterns, recognize anomalies, and respond to threats in real time. While AI systems are costly and complex, their advantages in reducing false positives and handling zero-day attacks make them essential. The integration of AI with traditional methods can enhance security, combining strengths for a proactive defense against evolving cyber threats. 

Comparative Analysis of Traditional vs. AI-Driven Network Security

In the past decades, accelerated by the recent COVID pandemic, the field of healthcare has faced technological advancements, such as wearables and mobile applications, that collect personal or health data. However, such tools are ineffective if they are not adopted by a large part of the population or if relevant health data, collected by the application, are not (voluntarily) shared. This study assessed the role of disease severity and evidence base for the effectiveness of the technology in the Privacy Calculus risk-benefit trade-off to contribute or hinder technology acceptance and data sharing. A large-scale 2x2x2 online vignette experiment (n = 822) was carried out, where participants were presented with a hypothetical scenario describing a novel health technology for diagnosing and tracking of infectious diseases. The results indicated that participants' privacy concerns negatively affected their intention to use the technology and willingness to share data, and that a high severity of the disease weakened this relationship. None of the other expected effects on intentions to use, willingness to share data or privacy concerns, were significant. These findings highlight the role of privacy as a barrier to technology acceptance, and suggest disease severity plays a role in the Privacy Calculus risk-benefit trade off by weakening the negative effect of privacy concerns on adoption in contexts where disease severity is high. 

The Privacy Calculus in the context of novel health technology for diagnosing and tracking infectious diseases: the role of disease severity and technology’s evidence base for effectiveness in adoption and voluntary health data-sharing

The Internet along with innovations in technology have inspired an industry focused on designing portable devices, known as wearables that can track users’ personal activities and wellbeing. While such technologies have many benefits, they also have risks (especially regarding information privacy and security). These concerns become even more pronounced with healthcare-related wearables. Consequently, users must consider the benefits given the risks (privacy calculus); however, users often opt for wearables despite their disclosure concerns (privacy paradox). In this study, we investigate the multidimensional role that privacy (and, in particular, the privacy calculus and the privacy paradox) plays in consumers’ intention to disclose their personal information, whether health status has a moderating effect on the relationship, and the influence of privacy on acceptance. To do so, we evaluated a research model that explicitly focused on the privacy calculus and the privacy paradox in the healthcare wearables acceptance domain. We used a survey-oriented approach to collect data from 225 users and examined relationships among privacy, health, and acceptance constructs. In that regard, our research confirmed significant evidence of the influence of the privacy calculus on disclosure and acceptance as well as evidence of the privacy paradox when considering health status. We found that consumers felt less inclined to disclose their personal information when the risks to privacy outweighed benefits; however, health status moderated this behavior such that people with worse health tipped the scale towards disclosure. This study expands our previous knowledge about healthcare wearables’ privacy/acceptance paradigm and, thus, the influences that affect healthcare wearables’ acceptance in the privacy context. 

The Role of the Privacy Calculus and the Privacy Paradox in the Acceptance of Wearables for Health and Wellbeing

A struggle has emerged in relation to the sacredness of one's private information and the importance of moving forward in a digital world of social media, smart devices, and Big Data -- an era known as the Age of Context. The purpose of this paper is to make a clear case for concern regarding the seriousness of keeping data private while facilitating efforts to encourage and support emerging technologies. This investigative process included the pursuit of relevant articles and published works that provided a clear and relevant picture of the current state of affairs concerning Big Data and privacy. After a review of the literature, an analysis of data collection methods, a discovery of Big Data processes and purposes, and the identification of risks pertaining to the individual, the military, and the country, it was determined that significant concerns do exist pertaining to data collection, Big Data, and privacy. These concerns not only pertain to the individual, but with military effectiveness and national security.

On the Intersection of Big Data and Privacy

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The Role of the Privacy Calculus and the Privacy Paradox in the Acceptance of Wearables for Health and Wellbeing

On the Intersection of Big Data and Privacy