skip to main content


Title: Local Information Privacy and Its Application to Privacy-Preserving Data Aggregation
Award ID(s):
1715947 1651492 2100013 1731164
NSF-PAR ID:
10292675
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE Transactions on Dependable and Secure Computing
ISSN:
1545-5971
Page Range / eLocation ID:
1 to 1
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Widespread deployment of Intelligent Infrastructure and the In- ternet of Things creates vast troves of passively-generated data. These data enable new ubiquitous computing applications—such as location-based services—while posing new privacy threats. In this work, we identify challenges that arise in applying use-based privacy to passively-generated data, and we develop Ancile, a plat- form that enforces use-based privacy for applications that consume this data. We find that Ancile constitutes a functional, performant platform for deploying privacy-enhancing ubiquitous computing applications. 
    more » « less
  2. The emergent, dynamic nature of privacy concerns in a shifting sociotechnical landscape creates a constant need for privacy-related resources and education. One response to this need is community-based privacy groups. We studied privacy groups that host meetings in diverse urban communities and interviewed the meeting organizers to see how they grapple with potentially varied and changeable privacy concerns. Our analysis identified three features of how privacy groups are organized to serve diverse constituencies: situating (finding the right venue for meetings), structuring (finding the right format/content for the meeting), and providing support (offering varied dimensions of assistance). We use these findings to inform a discussion of "privacy pluralism" as a perennial challenge for the HCI privacy research community, and we use the practices of privacy groups as an anchor for reflection on research practices. 
    more » « less
  3. 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