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Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information

Julia Hanson, Miranda Wei, Sophie Veys, Matthew Kugler, Lior Jacob Strahilevitz, Blase Ur

2020Year
25Citations
10Top-tier citations

Abstract

Data brokers and advertisers increasingly collect data in one context and use it in another. When users encounter a misuse of their data, do they subsequently disclose less information? We report on human-subjects experiments with 25 in-person and 280 online participants. First, participants provided personal information amidst distractor questions. A week later, while participants completed another survey, they received either a robotext or online banner ad seemingly unrelated to the study. Half of the participants received an ad containing their name, partner's name, preferred cuisine, and location; others received a generic ad. We measured how many of 43 potentially invasive questions participants subsequently chose to answer. Participants reacted negatively to the personalized ad, yet answered nearly all invasive questions accurately. We unpack our results relative to the privacy paradox, contextual integrity, and power dynamics in crowdworker platforms.

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