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Title: A Tale of Two Communities: Privacy of Third Party App Users in Crowdsourcing - The Case of Receipt Transcription

Mobile and web apps are increasingly relying on the data generated or provided by users such as from their uploaded documents and images. Unfortunately, those apps may raise significant user privacy concerns. Specifically, to train or adapt their models for accurately processing huge amounts of data continuously collected from millions of app users, app or service providers have widely adopted the approach of crowdsourcing for recruiting crowd workers to manually annotate or transcribe the sampled ever-changing user data. However, when users' data are uploaded through apps and then become widely accessible to hundreds of thousands of anonymous crowd workers, many human-in-the-loop related privacy questions arise concerning both the app user community and the crowd worker community. In this paper, we propose to investigate the privacy risks brought by this significant trend of large-scale crowd-powered processing of app users' data generated in their daily activities. We consider the representative case of receipt scanning apps that have millions of users, and focus on the corresponding receipt transcription tasks that appear popularly on crowdsourcing platforms. We design and conduct an app user survey study (n=108) to explore how app users perceive privacy in the context of using receipt scanning apps. We also design and conduct a crowd worker survey study (n=102) to explore crowd workers' experiences on receipt and other types of transcription tasks as well as their attitudes towards such tasks. Overall, we found that most app users and crowd workers expressed strong concerns about the potential privacy risks to receipt owners, and they also had a very high level of agreement with the need for protecting receipt owners' privacy. Our work provides insights on app users' potential privacy risks in crowdsourcing, and highlights the need and challenges for protecting third party users' privacy on crowdsourcing platforms. We have responsibly disclosed our findings to the related crowdsourcing platform and app providers.

 
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Award ID(s):
2246143
NSF-PAR ID:
10488757
Author(s) / Creator(s):
; ;
Publisher / Repository:
Association for Computing Machinery
Date Published:
Journal Name:
Proceedings of the ACM on Human-Computer Interaction
Volume:
7
Issue:
CSCW2
ISSN:
2573-0142
Page Range / eLocation ID:
1 to 43
Subject(s) / Keyword(s):
["Privacy","App User","Crowdsourcing","Receipt Transcription"]
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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