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Confident Privacy Decision-Making in IoT Environments

Published: 14 December 2019 Publication History

Abstract

Researchers are building Internet of Things (IoT) systems that aim to raise users’ privacy awareness, so that these users can make informed privacy decisions. However, there is a lack of empirical research on the practical implications of informed privacy decision-making in IoT. To gain deeper insights into this question, we conducted an online study (N = 488) of people’s privacy decision-making as well as their levels of privacy awareness toward diverse IoT service scenarios. Statistical analysis on the collected data confirmed that people who are well aware of potential privacy risks in a scenario tend to make more conservative and confident privacy decisions. Machine learning (ML) experiments also revealed that individuals overall privacy awareness is the most important feature when predicting their privacy decisions. We verified that ML models trained on privacy decisions made with confidence can produce highly accurate privacy recommendations for users (area under the ROC curve (AUC) of 87%). Based on these findings, we propose functional requirements for privacy-aware systems to facilitate well-informed privacy decision-making in IoT, which results in conservative and confident decisions that enjoy high consistency.

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cover image ACM Transactions on Computer-Human Interaction
ACM Transactions on Computer-Human Interaction  Volume 27, Issue 1
February 2020
206 pages
ISSN:1073-0516
EISSN:1557-7325
DOI:10.1145/3372746
Issue’s Table of Contents
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Publication History

Published: 14 December 2019
Accepted: 01 September 2019
Revised: 01 July 2019
Received: 01 December 2018
Published in TOCHI Volume 27, Issue 1

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Author Tags

  1. IoT
  2. decision confidence
  3. informed privacy decision-making
  4. privacy awareness
  5. privacy decision support
  6. privacy-aware system
  7. random effects model
  8. random forest

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  • NSF

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