Creating Effective Labels
summary
Study builds modular privacy labels and tests them with an IoT marketplace and user experiments, reporting concrete effects: a willingness to accept framing raises price users will pay for privacy by about 30%, while a willingness‑to‑pay framing shows no difference from control. Positive framing (padlock) elicits more privacy‑preserving choices than negative framing (eye). Only 11% of users worldwide enable app tracking by default. A machine‑learning model predicts comfort sharing data from five factors to produce personalized five‑point ratings.
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chapters (8)
- 0:00Start
- 1:27Building Blocks for Effective Labels
- 1:52Information Asymmetry
- 3:07Reducing Cognitive Burden
- 3:49Modular Labels
- 4:41Psychological Biases
- 7:09IoTMarketplace Example
- 9:28Personalization
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