skip to main content


Title: Exploring Defaults and Framing effects on Privacy Decision Making in Smarthomes
Research has shown that privacy decisions are affected by heuristic influences such as default settings and framing, and such effects are likely also present in smarthome privacy de- cisions. In this paper we pose the challenge question: How exactly do defaults and framing influence smarthome users’ privacy decisions? We conduct a large-scale scenario-based study with a mixed fractional factorial design, and use sta- tistical analysis and machine learning to investigate these effects. We discuss the implications of our findings for the designers of smarthome privacy-setting interfaces.  more » « less
Award ID(s):
1640664
NSF-PAR ID:
10072411
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the SOUPS 2018 Workshop on the Human aspects of Smarthome Security and Privacy (WSSP)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The Internet of Things provides household device users with an ability to connect and manage numerous devices over a common platform. However, the sheer number of possible privacy settings creates issues such as choice overload. This article outlines a data-driven approach to understand how users make privacy decisions in household IoT scenarios. We demonstrate that users are not just influenced by the specifics of the IoT scenario, but also by aspects immaterial to the decision, such as the default setting and its framing. 
    more » « less
  2. COVID-19 exposure-notification apps have struggled to gain adoption. Existing literature posits as potential causes of this low adoption: privacy concerns, insufficient data transparency, and the type of appeal – collective- vs. individual-good – used to frame the app. As policy guidance suggests using tailored advertising to evaluate the effects of these factors, we present the first field study of COVID-19 contact tracing apps with a randomized, control trial of 14 different advertisements for CovidDefense, Louisiana’s COVID-19 exposure-notification app. We find that all three hypothesized factors – privacy, data transparency, and appeals framing – relate to app adoption, even when controlling for age, gender, and community density. Our results offer (1) the first field evidence supporting the use of collective-good appeals, (2) nuanced findings regarding the efficacy of data and privacy transparency, the effects of which are moderated by appeal framing and potential users’ demographics, and (3) field-evidence-based guidance for future efforts to encourage pro-social health technology adoption. 
    more » « less
  3. We examined whether framing younger and older adults learning goals in terms of maximizing gains or minimizing losses impacts their ability to selectively remember high-value information. Specifically, we presented younger and older adults with lists of words paired with point values and participants were either told that they would receive the value associated with each word if they recalled it on a test or that they would lose the points associated with each word if they failed to recall it on the test. We also asked participants to predict the likelihood of recalling each word to deter- mine if younger and older adults were metacognitively aware of any potential framing effects. Results revealed that older adults expected to be more selective when their goals were framed in terms of losses, but younger adults expected to be more selective when their goals were framed in terms of gains. However, this was not the case as both younger and older adults were more selective for high-value informa- tion when their goals were framed in terms of maximizing gains compared with minimizing losses. Thus, the framing of learning goals can impact metacognitive decisions and subsequent memory in both younger and older adults. 
    more » « less
  4. Background Developers, designers, and researchers use rapid prototyping methods to project the adoption and acceptability of their health intervention technology (HIT) before the technology becomes mature enough to be deployed. Although these methods are useful for gathering feedback that advances the development of HITs, they rarely provide usable evidence that can contribute to our broader understanding of HITs. Objective In this research, we aim to develop and demonstrate a variation of vignette testing that supports developers and designers in evaluating early-stage HIT designs while generating usable evidence for the broader research community. Methods We proposed a method called health concept surveying for untangling the causal relationships that people develop around conceptual HITs. In health concept surveying, investigators gather reactions to design concepts through a scenario-based survey instrument. As the investigator manipulates characteristics related to their HIT, the survey instrument also measures proximal cognitive factors according to a health behavior change model to project how HIT design decisions may affect the adoption and acceptability of an HIT. Responses to the survey instrument were analyzed using path analysis to untangle the causal effects of these factors on the outcome variables. Results We demonstrated health concept surveying in 3 case studies of sensor-based health-screening apps. Our first study (N=54) showed that a wait time incentive could influence more people to go see a dermatologist after a positive test for skin cancer. Our second study (N=54), evaluating a similar application design, showed that although visual explanations of algorithmic decisions could increase participant trust in negative test results, the trust would not have been enough to affect people’s decision-making. Our third study (N=263) showed that people might prioritize test specificity or sensitivity depending on the nature of the medical condition. Conclusions Beyond the findings from our 3 case studies, our research uses the framing of the Health Belief Model to elicit and understand the intrinsic and extrinsic factors that may affect the adoption and acceptability of an HIT without having to build a working prototype. We have made our survey instrument publicly available so that others can leverage it for their own investigations. 
    more » « less
  5. null (Ed.)
    Differential privacy protects an individual's privacy by perturbing data on an aggregated level (DP) or individual level (LDP). We report four online human-subject experiments investigating the effects of using different approaches to communicate differential privacy techniques to laypersons in a health app data collection setting. Experiments 1 and 2 investigated participants' data disclosure decisions for low-sensitive and high-sensitive personal information when given different DP or LDP descriptions. Experiments 3 and 4 uncovered reasons behind participants' data sharing decisions, and examined participants' subjective and objective comprehensions of these DP or LDP descriptions. When shown descriptions that explain the implications instead of the definition/processes of DP or LDP technique, participants demonstrated better comprehension and showed more willingness to share information with LDP than with DP, indicating their understanding of LDP's stronger privacy guarantee compared with DP. 
    more » « less