Privacy-Preserving Streaming Truth Discovery in Crowdsourcing With Differential Privacy

Dan Wang

Abstract:

Differential privacy (DP) has gained popularity in truth discovery recently due to its strong privacy guarantee. However, existing
DP mechanisms for streaming data publication are not suitable for truth discovery as they fail to consider the different reliabilities of
individuals, while the DP-based approaches for truth discovery are not suitable for streaming data because they ignore the correlations
between truths over time. Directly applying these existing methods to streaming crowdsourced data would lead to low accuracy of the
discovered truth. To solve this problem, in this paper, we propose an edge computing based privacy-preserving truth discovery
mechanism, named PrivSTD, for streaming crowdsourced data to realize high accuracy of discovered truth while protecting the privacy of
workers. Specifically, edge servers are introduced between the untrusted cloud server and workers to securely calculate the local truths
and workers’ reliabilities. A truth-dependent budget recycle mechanism is proposed for each edge server to adaptively determine the
perturbed timestamp and allocate the privacy budget according to the changing pattern of local truths. Besides, a reliability-based
perturbation mechanism is proposed to reduce the perturbation magnitude on the basis of worker’s reliability. We theoretical analyze the
data utility and computation cost of PrivSTD, and prove that PrivSTD can satisfy w-event ($\epsilon$, $\delta$)-differential privacy. Extensive experimental results on synthetic and real-world datasets demonstrate that PrivSTD achieves better utility than the state-of-the-art approaches.