NDLI: Information extraction and manipulation threats in crowd-powered systems

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Information extraction and manipulation threats in crowd-powered systems

Content Provider	ACM Digital Library
Author	Lasecki, Walter S. Kamar, Ece Teevan, Jaime
Abstract	Crowd-powered systems have become a popular way to augment the capabilities of automated systems in real-world settings. Many of these systems rely on human workers to process potentially sensitive data or make important decisions. This puts these systems at risk of unintentionally releasing sensitive data or having their outcomes maliciously manipulated. While almost all crowd-powered approaches account for errors made by individual workers, few factor in active attacks on the system. In this paper, we analyze different forms of threats from individuals and groups of workers extracting information from crowd-powered systems or manipulating these systems' outcomes. Via a set of studies performed on Amazon's Mechanical Turk platform and involving 1,140 unique workers, we demonstrate the viability of these threats. We show that the current system is vulnerable to coordinated attacks on a task based on the requests of another task and that a significant portion of Mechanical Turk workers are willing to contribute to an attack. We propose several possible approaches to mitigating these threats, including leveraging workers who are willing to go above and beyond to help, automatically flagging sensitive content, and using workflows that conceal information from each individual, while still allowing the group to complete a task. Our findings enable the crowd to continue to play an important part in automated systems, even as the data they use and the decisions they support become increasingly important.
Starting Page	248
Ending Page	256
Page Count	9
File Format	PDF
ISBN	9781450325400
DOI	10.1145/2531602.2531733
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Crowdsourcing Privacy Manipulation. Extraction Security
Content Type	Text
Resource Type	Article

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