skip to main content

Explore Research Products in the NSF-PAR

Title: A User Study of a Wearable System to Enhance Bystanders’ Facial Privacy
The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called FacePET developed to enhance bystanders’ facial privacy by providing a way for bystanders to protect their own privacy rather than relying on external systems for protection. While past works in the literature focused on privacy perceptions of bystanders when photographed in public/shared spaces, there has not been research with a focus on user perceptions of bystander-based wearable devices to enhance privacy. Thus, in this work, we focus on user perceptions of the FacePET device and/or similar wearables to enhance bystanders’ facial privacy. In our study, we found that 16 participants would use FacePET or similar devices to enhance their facial privacy, and 17 participants agreed that if smart glasses had features to conceal users’ identities, it would allow them to become more popular.  more » « less
Award ID(s):
1950416
NSF-PAR ID:
10205752
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
IoT
Volume:
1
Issue:
2
ISSN:
2624-831X
Page Range / eLocation ID:
198 to 217
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , JMIR Mental Health)
    Background

    Mobile mental health systems (MMHS) have been increasingly developed and deployed in support of monitoring, management, and intervention with regard to patients with mental disorders. However, many of these systems rely on patient data collected by smartphones or other wearable devices to infer patients’ mental status, which raises privacy concerns. Such a value-privacy paradox poses significant challenges to patients’ adoption and use of MMHS; yet, there has been limited understanding of it.

     Objective

    To address the significant literature gap, this research aims to investigate both the antecedents of patients’ privacy concerns and the effects of privacy concerns on their continuous usage intention with regard to MMHS.

     Methods

    Using a web-based survey, this research collected data from 170 participants with MMHS experience recruited from online mental health communities and a university community. The data analyses used both repeated analysis of variance and partial least squares regression.

     Results

    The results showed that data type (P=.003), data stage (P<.001), privacy victimization experience (P=.01), and privacy awareness (P=.08) have positive effects on privacy concerns. Specifically, users report higher privacy concerns for social interaction data (P=.007) and self-reported data (P=.001) than for biometrics data; privacy concerns are higher for data transmission (P=.01) and data sharing (P<.001) than for data collection. Our results also reveal that privacy concerns have an effect on attitude toward privacy protection (P=.001), which in turn affects continuous usage intention with regard to MMHS.

     Conclusions

    This study contributes to the literature by deepening our understanding of the data value-privacy paradox in MMHS research. The findings offer practical guidelines for breaking the paradox through the design of user-centered and privacy-preserving MMHS.

     
    more » « less
  2. ; ( , Education and information technologies)
    Wearable devices are a popular class of portable ubiquitous technology. These devices are available in a variety of forms, ranging from smart glasses to smart rings. The fact that smart wearable devices are attached to the body makes them particularly suitable to be integrated into people’s daily lives. Thus, we propose that wearables can be particularly useful to help people make sense of different kinds of information and situations in the course of their everyday activities, in other words, to help support learning in everyday life. Further, different forms of wearables have different affordances leading to varying perceptions and preferences, depending on the purpose and context of use. While there is research on wearable use in the learning context, it is mostly limited to specific settings and usually only explores wearable use for a specific task. This paper presents an online survey with 70 participants conducted to understand users’ preferences and perceptions of how wearables may be used to support learning in their everyday life. Multiple ways of use of wearable for learning were proposed. Asking for information was the most common learning-oriented use. The smartwatch/wristband, followed by the smart glasses, was the most preferred wearable form factor to support learning. Our survey results also showed that the choice of wearable type to use for learning is associated with prior wearable experience and that perceived social influence of wearables decreases significantly with gain in the experience with a fitness tracker. Overall, our study indicates that wearable devices have untapped potential to be used for learning in daily life and different form factors are perceived to afford different functions and used for different purposes. 
    more » « less
  3. ; ; ; ; ; ( , Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering)
    Hara, T. ; Yamaguchi, H. (Ed.)
    Prevalent wearables (e.g., smartwatches and activity trackers) demand high secure measures to protect users' private information, such as personal contacts, bank accounts, etc. While existing two-factor authentication methods can enhance traditional user authentication, they are not convenient as they require participations from users. Recently, manufacturing imperfections in hardware devices (e.g., accelerometers and WiFi interface) have been utilized for low-effort two-factor authentications. However, these methods rely on fixed device credentials that would require users to replace their devices once the device credentials are stolen. In this work, we develop a novel device authentication system, WatchID, that can identify a user's wearable using its vibration-based device credentials. Our system exploits readily available vibration motors and accelerometers in wearables to establish a vibration communication channel to capture wearables' unique vibration characteristics. Compared to existing methods, our vibration-based device credentials are reprogrammable and easy to use. We develop a series of data processing methods to mitigate the impact of noises and body movements. A lightweight convolutional neural network is developed for feature extraction and device authentication. Extensive experimental results using five smartwatches show that WatchID can achieve an average precision and recall of 98% and 94% respectively in various attacking scenarios. 
    more » « less
  4. ; ; ( , CHI Conference on Human Factors in Computing Systems CHI Conference on Human Factors in Computing Systems Extended Abstracts)
    null (Ed.)
    The proliferation of the Internet of Things (IoT) has started transforming our lifestyle through automation of home appliances. However, there are users who are hesitant to adopt IoT devices due to various privacy and security concerns. In this paper, we elicit peoples’ attitude and concerns towards adopting IoT devices. We conduct an online survey and collect responses from 232 participants from three different geographic regions (United States, Europe, and India); the participants consist of both adopters and non-adopters of IoT devices. Through data analysis, we determine that there are both similarities and differences in perceptions and concerns between adopters and non-adopters. For example, even though IoT and non-IoT users share similar security and privacy concerns, IoT users are more comfortable using IoT devices in private settings compared to non-IoT users. Furthermore, when comparing users’ attitude and concerns across different geographic regions, we found similarities between participants from the US and Europe, yet participants from India showcased contrasting behavior. For instance, we found that participants from India were more trusting in their government to properly protect consumer data and were more comfortable using IoT devices in a variety of public settings, compared to participants from the US and Europe. Based on our findings, we provide recommendations to reduce users’ concerns in adopting IoT devices, and thereby enhance user trust towards adopting IoT devices. 
    more » « less
  5. ; ; ; ( , JMIR mHealth and uHealth)
    null (Ed.)
    Background The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. Objective We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. Methods We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. Results In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. Conclusions Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email