Introduction: With the COVID-19 outbreak, South Korea has been making contact trace data public to help people self-check if they have been in contact with a person infected with the coronavirus. Despite its benefits in suppressing the spread of the virus, publicizing contact trace data raises concerns about individuals’ privacy. In view of this tug-of-war between one’s privacy and public safety, this work aims to deepen the understanding of privacy risks of contact trace data disclosure practices in South Korea. Method: In this study, publicly available contact trace data of 970 confirmed patients were collected from seven metropolitan cities in South Korea (20th Jan-20th Apr 2020). Then, an ordinal scale of relative privacy risk levels was introduced for evaluation, and the assessment was performed on the personal information included in the contact trace data such as demographics, significant places, sensitive information, social relationships, and routine behaviors. In addition, variance of privacy risk levels was examined across regions and over time to check for differences in policy implementation. Results: It was found that most of the contact trace data showed the gender and age of the patients. In addition, it disclosed significant places (home/work) ranging across different levels of privacy risks in over 70% of the cases. Inference on sensitive information (hobby, religion) was made possible, and 48.7% of the cases exposed the patient’s social relationships. In terms of regional differences, a considerable discrepancy was found in the privacy risk for each category. Despite the recent release of government guidelines on data disclosure, its effects were still limited to a few factors (e.g., workplaces, routine behaviors). Discussion: Privacy risk assessment showed evidence of superfluous information disclosure in the current practice. This study discusses the role of "identifiability" in contact tracing to provide new directions for minimizing disclosure of privacy infringing information. Analysis of real-world data can offer potential stakeholders, such as researchers, service developers, and government officials with practical protocols/guidelines in publicizing information of patients and design implications for future systems (e.g., automatic privacy sensitivity checking) to strike a balance between one’s privacy and the public benefits with data disclosure.

People with mental health issues often stay indoors, reducing their outdoor activities. This situation emphasizes the need for self-tracking technology in homes for mental health research, offering insights into their daily lives and potentially improving care. This study leverages a multimodal smart speaker to design a proactive self-tracking research system that delivers mental health surveys using an experience sampling method (ESM). Our system determines ESM delivery timing by detecting user context transitions and allowing users to answer surveys through voice dialogues or touch interactions. Furthermore, we explored the user experience of a proactive self-tracking system by conducting a four-week field study (n=20). Our results show that context transition-based ESM delivery can increase user compliance. Participants preferred touch interactions to voice commands, and the modality selection varied depending on the user’s immediate activity context. We explored the design implications for home-based, context-aware self-tracking with multimodal speakers, focusing on practical applications.

The rapid expansion of remote work following the COVID-19 pandemic has necessitated the development of more robust and secure endpoint device security solutions. Companies have begun to adopt the zero trust security concept as an alternative to traditional network boundary security measures, which requires that every device and user be considered untrustworthy until proven otherwise. Despite the potential benefits of implementing zero trust, the stringent security measures can inadvertently lead to low availability by denying access to legitimate users or limiting their ability to access necessary resources. To address this challenge, we propose a risk-scoring algorithm that balances confidentiality and availability by evaluating the user’s impact on resources. Our contributions include (1) summarizing the limitations of existing risk scoring systems in companies that implement zero trust, (2) proposing a dynamic importance metric that measures the importance of resources accessible to users within zero trust systems, and (3) introducing a risk-scoring algorithm that employs the dynamic importance metric to enhance both security and availability in zero trust environments. By incorporating the dynamic importance metric, our proposed algorithm provides a more accurate representation of risk, leading to better security decisions and improved resource availability for legitimate users. This proposal aims to help organizations achieve a more balanced approach to endpoint device security, addressing the unique challenges posed by the increasing prevalence of remote work.

Automated driving systems enable drivers to perform various non-driving tasks, which has led to concerns regarding driver distraction during automated driving. These concerns have spurred numerous studies investigating driver performance of fallback to driving (i.e., takeover). However, publicly available datasets that present takeover performance data are insufficient. The lack of datasets limits advancements in developing safe automated driving systems. This study introduces TD2D, a dataset collected from 50 drivers with balanced gender representation and diverse age groups in an L2 automated driving simulator. The dataset comprises 500 cases including takeover performance, workload, physiological, and ocular data collected across 10 secondary task conditions: (1) no secondary tasks, (2) three visual tasks, and (3) six auditory tasks. We anticipate that this dataset will contribute significantly to the advancement of automated driving systems.