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

Privacy and Security and Artificial Intelligence

Article Snapshot

Author(s)

Lujo Bauer, Richard Chow, Lorrie Faith Cranor, Martin Degeling, Pardis Emami Naeini, Heather Patterson and Mohammad Reza Haghighat

Source

Proceedings of the ACM on Human-Computer Interaction, Vol. 2, No. CSCW, Article 48, November, 2018

Summary

People find making privacy decisions about Internet of Things (IoT) devices difficult. Users’ decisions about privacy can be swayed by experts and friends.

Policy Relevance

Presenting information about others’ choices can help users make privacy decisions.

Main Points

  • Privacy assistants built into devices such as smartphones and smartwatches can help users manage privacy decisions by presenting helpful social cues.
     
  • Study participants were given different data collection scenarios and asked whether they would allow or disallow data collection.
     
    • Some scenarios were "allow" scenarios (80% of people in a pre-study survey allowed data collection).
       
    • Some were "deny" scenarios (less than 20% of people in a pre-study survey allowed data collection).
       
    • Some were balanced (45-55% of people in a pre-study survey allowed data collection).
       
  • In each scenario, participants were told the percentage of influencers (either “privacy experts” or “friends who use this app”) who allowed data collection.
     
    • In “consistent” scenarios, participants were told that most influencers allowed data collection in “allow” scenarios, or denied in “deny” scenarios.
       
    • In “inconsistent” scenarios, participants were told that most influencers allowed data collection in “deny” scenarios, or denied collection in “allow” scenarios.
       
  • Participants made faster decisions about privacy when provided with social cues; generally, they decided faster in “allow” scenarios than in “deny” scenarios.
     
  • Most participants were less influenced by social cues in inconsistent scenarios, especially when the influencer was a privacy expert.
     
    • In consistent scenarios, people were more likely to follow social cues, reflecting confirmation bias.
       
    • If influencers repeatedly allowed collection in “deny” scenarios or denied it in “allow” scenarios, participants were less likely to be influenced in later scenarios.
       
  • In “allow” scenarios, where the benefits of collection outweighed risks, people were more influenced by cues from privacy experts; where risks outweighed benefits, people were more influenced by friends.
     
  • In “balanced” scenarios, which present trade-offs between benefits and risks, people are more likely to be influenced by social cues.
     

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