"Our privacy needs to be protected at all costs": Crowd workers' privacy experiences on Amazon Mechanical Turk

Huichuan Xia, Yang Wang, Yun Huang, Anuj Shah

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are widely used by organizations, researchers, and individuals to outsource a broad range of tasks to crowd workers. Prior research has shown that crowdsourcing can pose privacy risks (e.g., de-anonymization) to crowd workers. However, little is known about the specific privacy issues crowd workers have experienced and how they perceive the state of privacy in crowdsourcing. In this paper, we present results from an online survey of 435 MTurk crowd workers from the US, India, and other countries and areas. Our respondents reported different types of privacy concerns (e.g., data aggregation, profiling, scams), experiences of privacy losses (e.g., phishing, malware, stalking, targeted ads), and privacy expectations on MTurk (e.g., screening tasks). Respondents from multiple countries and areas reported experiences with the same privacy issues, suggesting that these problems may be endemic to the whole MTurk platform. We discuss challenges, high-level principles and concrete suggestions in protecting crowd workers' privacy on MTurk and in crowdsourcing more broadly.

Original languageEnglish (US)
Article number113
JournalProceedings of the ACM on Human-Computer Interaction
Volume1
Issue numberCSCW
DOIs
StatePublished - Nov 2017

Keywords

  • Amazon mechanical turk (MTurk)
  • Crowdsourcing
  • Privacy

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Human-Computer Interaction
  • Social Sciences (miscellaneous)

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