Group-Level Frameworks for Data Ethics, Privacy, Safety and Security in Digital Environments

In today\u27s digital age, the widespread collection, utilization, and sharing of personal data are challenging our conventional beliefs about privacy and information security. This thesis will explore the boundaries of conventional privacy and security frameworks and investigate new methods to handle online privacy by integrating groups. Additionally, we will examine approaches to monitoring the types of information gathered on individuals to tackle transparency concerns in the data broker and data processor sector. We aim to challenge traditional notions of privacy and security to encourage innovative strategies for safeguarding them in our interconnected, dispersed digital environment. This thesis uses a multi-disciplinary approach to complex systems, drawing from various fields such as data ethics, legal theory, and philosophy. Our methods include complex systems modeling, network analysis, data science, and statistics. As a first step, we investigate the limits of individual consent frameworks in online social media platforms. We develop new security settings, called distributed consent, that can be used in an online social network or coordinated across online platforms. We then model the levels of observability of individuals on the platform(s) to measure the effectiveness of the new security settings against surveillance from third parties. Distributed consent can help to protect individuals online from surveillance, but it requires a high coordination cost on the part of the individual. Users must also decide whether to protect their privacy from third parties and network neighbors by disclosing security settings or taking on the burden of coordinating security on single and multiple platforms. However, the coordination burden may be more appropriate for systems-level regulation. We then explore how groups of individuals can work together to protect themselves from the harms of misinformation on online social networks. Social media users are not equally susceptible to all types of misinformation. Further, diverse groups of social media communities can help protect one another from misinformation by correcting each other\u27s blind spots. We highlight the importance of group diversity in network dynamics and explore how natural diversity within groups can provide protection rather than relying on new technologies such as distributed consent settings. Finally, we investigate methods to interrogate what types of personal data are collected by third parties and measure the risks and harms associated with aggregating personal data. We introduce methods that provide transparency into how modern data collection practices pose risks to data subjects online. We hope that the collection of these results provides a humble step toward revealing gaps in privacy and security frameworks and promoting new solutions for the digital age

Diverse Misinformation: Impacts of Human Biases on Detection of Deepfakes on Networks

Social media platforms often assume that users can self-correct against misinformation. However, social media users are not equally susceptible to all misinformation as their biases influence what types of misinformation might thrive and who might be at risk. We call "diverse misinformation" the complex relationships between human biases and demographics represented in misinformation. To investigate how users' biases impact their susceptibility and their ability to correct each other, we analyze classification of deepfakes as a type of diverse misinformation. We chose deepfakes as a case study for three reasons: 1) their classification as misinformation is more objective; 2) we can control the demographics of the personas presented; 3) deepfakes are a real-world concern with associated harms that must be better understood. Our paper presents an observational survey (N=2,016) where participants are exposed to videos and asked questions about their attributes, not knowing some might be deepfakes. Our analysis investigates the extent to which different users are duped and which perceived demographics of deepfake personas tend to mislead. We find that accuracy varies by demographics, and participants are generally better at classifying videos that match them. We extrapolate from these results to understand the potential population-level impacts of these biases using a mathematical model of the interplay between diverse misinformation and crowd correction. Our model suggests that diverse contacts might provide "herd correction" where friends can protect each other. Altogether, human biases and the attributes of misinformation matter greatly, but having a diverse social group may help reduce susceptibility to misinformation.Comment: Supplementary appendix available upon request for the time bein

Dark Data: making dark data FAIR

Because the FAIR principles emphasize usability and accessibility of data, applying those principles to data that shouldn\u27t exist or shouldn\u27t persist increase the potential for harm. The goal of reducing how much data remains dark is a valuable one, but only if the data have appropriate safeguards against inappropriate use. We thus argue in this paper that the goals of the Making Dark Data Fair project are important goals, but only if they include explicit considerations of the necessary safeguards to ensure that the use of the data comply with all legal and moral obligations. In this paper we will discuss ethical considerations of dark data and evaluate computing and procedural solutions for a fair and ethical data stewardship pipeline

Detecting stress in college freshmen from wearable sleep data

Sleep plays an important role in health and functioning, yet little is known about how it is linked to stress in young adults. Consumer wearables have been particularly successful at quantifying sleep and may be useful in identifying changes in mental health. The transition to college has a notable impact on both stress and sleep behaviors. Students from a public university provided continuous biometric data and answered weekly surveys for their first semester of college (N=507). Longitudinal models showed that increased average respiratory rate during sleep was associated with higher weekly perceived stress scores, effects that persisted after controlling for gender, race, first-generation status, mental health history, trauma history. Specifically, for every increased breath per minute, the odds of experiencing moderate-to-high stress were 1.25 times higher (a 25% increase), holding demographic and psychological history variables constant. Notably, relationships with stress were specific to respiratory rate, but were not found with other sleep measures previously linked to stress, such as heart rate variability, total sleep hours, sleep efficiency, sleep onset latency, and wake up count. Consistent with previous work, female gender, a previous mental health diagnosis, and previous exposure to multiple traumas were significantly associated with self-reported stress. These findings point to respiratory rate as a potentially important factor to measure due to its robust association with stress among college students. Wearable data may help us identify, understand, and to better predict stress, a strong signal of the ongoing mental health epidemic among college students

Events and Behaviors Associated with Symptoms of Generalized Anxiety Disorder in First-year College Students

Objective Identifying associations between individual characteristics, psychological history, academic stressors, and the trajectory of generalized anxiety disorder symptoms in a college cohort may provide insight into the design of mental health interventions. Method We recruited first-year undergraduate students between the ages of 18-24 at a university in the northeastern United States. Following consent, enrolled participants completed surveys between October 21, 2022 - December 12, 2022 on self-reported exposures to potentially traumatic life events, mental health history, and stressors. The main outcomes of interest were the occurrence, persistence, development, and reduction in generalized anxiety disorder (GAD) symptoms using the Generalized Anxiety Disorder Questionnaire (GAD-7). Results A previous anxiety disorder diagnosis was associated with occurrence (adjusted odds ratio [AOR], 2.10; 95% CI, 1.51-2.93), persistence (incidence rate ratio [IRR], 1.64; 95% CI, 1.29-2.09), and duration of GAD symptoms during the study (IRR, 2.90; 95% CI, 1.30-6.46). Prior exposure to traumatic life events was associated with the occurrence (AOR, 1.80; 95% CI, 1.28-2.52) and persistence of GAD symptoms (IRR, 1.36; 95% CI, 1.06-1.74). The presence of a test or project was associated with occurrence (AOR, 1.68; 95% CI, 1.42-1.98), persistence (IRR, 2.09; 95% CI, 1.43-3.04), and development of GAD symptoms(AOR 1.58, 95% CI, 1.30-1.92), and decreased the likelihood of GAD symptom reduction (AOR, 0.69; 95% CI, 0.56-0.86). The personality trait of emotional stability decreased the likelihood of occurrence (AOR, 0.60; 95% CI, 0.50-0.67), persistence (AOR, 0.90 95% CI, 0.65- 0.78), development (AOR, 0.84; 95% CI, 0.77-0.92), and reduction in GAD symptoms (AOR, 0.82; 95% CI, 0.75-0.88). The personality trait of extraversion also decreased the likelihood of GAD symptoms (AOR, 0.89; 95% CI, 0.83-0.95). Conclusion These findings suggest that beginning college with a history of an anxiety disorder or prior exposure to multiple traumatic events increases the likelihood of occurrence and persistence of a GAD-7 score consistent with moderate-to-severe GAD symptoms. Personality traits also play a role in the occurrence and persistence of GAD symptoms, which may be important for personalizing psychological treatments. Academic stressors (i.e., a test or project), were significantly associated with the presence, persistence, development, and reduction in GAD-7 scores consistent with moderate-to-severe GAD symptoms