The Influence of Friends and Experts on Privacy Decision Making in IoT Scenarios

Abstract

As increasingly many Internet-of-Things (IoT) devices collect personal data, users face more privacy decisions. Personal privacy assistants can provide social cues and help users make informed decisions by presenting information about how others have decided in similar cases. To better understand which social cues are relevant and whose recommendations users are more likely to follow, we presented 1000 online participants with nine IoT data-collection scenarios. Some participants were told the percentage of experts or friends who allowed data collection in each scenario, while other participants were provided no social cue. At the conclusion of each scenario, participants were asked whether they would allow the described data collection. Our results help explain under what circumstances users are more or less likely to be swayed by the reported behavior of others in similar scenarios. For example, our results indicate that when friends denied data collection, our participants were more influenced than when friends allowed data collection. On the other hand, participants were more influenced by experts when they allowed data collection. We also observed that influence could get stronger or wear off when participants were exposed to a sequence of scenarios. For example, when experts and friends repeatedly allowed data collection in scenarios with clear risk or denied it in scenarios with clear benefits, participants were less likely to be influenced by them in subsequent scenarios. CCS Concepts: • Security and privacy → Social aspects of security and privacy; • Human-centered computing → User models;

Informing the Design of a Personalized Privacy Assistant for the Internet of Things

“…Users wanted to see both expert opinions (authoritative) as well as real users' opinions (crowd-sourced). Social cues and expert recommendations can have a significant impact on users' decisions [11]. Furthermore, users have varied preferences and acceptance of different sources [33] and these sources have inherent biases.…”

Incorporating individual and group privacy preferences in the internet of things

“…13: Total time for three situations of no-privacy privacy-no update & privacy-data update (Edge-managed\Privacy). decisions about their privacy decisions in an IoT environment (Emami Naeini et al, 2018). Despite the great benefits these studies provide, a missing piece of the puzzle is to observe the user behavior in a real IoT environment, as these studies depend largely on user questionnaires.…”

“…Fortunately, studies have shown that the privacy preferences can be predicted by observing the user behavior towards privacy using limited number of scenarios (Naeini et al, 2017). Also, social cues can be given to users to allow them to make informed decisions about their privacy decisions in an IoT environment (Emami Naeini et al, 2018). Despite the great benefits these studies provide, a missing piece of the puzzle is to observe the user behavior in a real IoT environment, as these studies depend largely on user questionnaires.…”

Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans

“…Researchers from the area of scenario-based survey have introduced many different approaches to create hypothetical scenarios using text, graphics, games, app interfaces, etc. [15,24,43,45]. Among all of these, text-based scenarios are preferred in case of surveying the participants.…”