A key challenge of big data analytics is how to collect a large volume of
(labeled) data. Crowdsourcing aims to address this challenge via aggregating
and estimating high-quality data (e.g., sentiment label for text) from
pervasive clients/users. Existing studies on crowdsourcing focus on designing
new methods to improve the aggregated data quality from unreliable/noisy
clients. However, the security aspects of such crowdsourcing systems remain
under-explored to date. We aim to bridge this gap in this work. Specifically,
we show that crowdsourcing is vulnerable to data poisoning attacks, in which
malicious clients provide carefully crafted data to corrupt the aggregated
data. We formulate our proposed data poisoning attacks as an optimization
problem that maximizes the error of the aggregated data. Our evaluation results
on one synthetic and two real-world benchmark datasets demonstrate that the
proposed attacks can substantially increase the estimation errors of the
aggregated data. We also propose two defenses to reduce the impact of malicious
clients. Our empirical results show that the proposed defenses can
substantially reduce the estimation errors of the data poisoning attacks.

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