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

要旨

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.

受賞
Honorable Mention
キーワード
Hyper-Personalization
Targeted Advertising
Creepy
User Study
著者
Julia Hanson
University of Chicago, Chicago, IL, USA
Miranda Wei
University of Washington & University of Chicago, Seattle, WA, USA
Sophie Veys
University of Chicago, Chicago, IL, USA
Matthew Kugler
Northwestern University, Chicago, IL, USA
Lior Strahilevitz
University of Chicago, Chicago, IL, USA
Blase Ur
University of Chicago, Chicago, IL, USA
DOI

10.1145/3313831.3376415

論文URL

https://doi.org/10.1145/3313831.3376415

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Privacy theory & information disclosure

Paper session
313B O'AHU
5 件の発表
2020-04-30 20:00:00
2020-04-30 21:15:00
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