Gig Work at What Cost? Exploring Privacy Risks of Gig Work Platform Participation in the U.S.
Authors: Amogh Pradeep (Northeastern University), Johanna Gunawan (Northeastern University), Álvaro Feal (Northeastern University), Woodrow Hartzog (Boston University), David Choffnes (Northeastern University)
Volume: 2025
Issue: 1
Pages: 491–510
DOI: https://doi.org/10.56553/popets-2025-0027
Abstract: In recent years, "gig work" platforms have gained popularity as a way for individuals to earn money; as of 2021, 16% of Americans have at some point earned money from such platforms. Despite their popularity and their history of unfair data collection practices and worker safety, little is known about the data collected from workers (and users) by gig platforms and about the privacy dark pattern designs present in their apps.
This paper presents an empirical measurement of 16 gig work platforms' data practices in the U.S. We analyze what data is collected by these platforms, and how it is shared and used. Finally, we consider how these practices constitute privacy dark patterns. To that end, we develop a novel combination of methods to address gig-worker-specific challenges in experimentation and data collection, enabling the largest in-depth study of such platforms to date. We find extensive data collection and sharing with 60 third parties—including sharing reversible hashes of worker Social Security Numbers (SSNs)—along with dark patterns that subject workers to greater privacy risk and opportunistically use collected data to nag workers in off-platform messages. We conclude this paper with proposed interdisciplinary mitigations for improving gig worker privacy protections. After we disclosed our SSN-related findings to affected platforms, the platforms confirmed that the issue had been mitigated. This is consistent with our independent audit of the affected platforms. Analysis code and redacted datasets will be made available to those who wish to reproduce our findings.
Keywords: gig work, privacy, tracking, dark patterns
Copyright in PoPETs articles are held by their authors. This article is published under a Creative Commons Attribution 4.0 license.
