  Crowdsourcing has arisen as a new problem-solving paradigm for tasks that are
difficult for computers but easy for humans. However, since the answers
collected from the recruited participants (workers) may contain sensitive
information, crowdsourcing raises serious privacy concerns. In this paper, we
investigate the problem of protecting answer privacy under local differential
privacy (LDP), by which individual workers randomize their answers
independently and send the perturbed answers to the task requester. The utility
goal is to enable to infer the true answer (i.e., truth) from the perturbed
data with high accuracy. One of the challenges of LDP perturbation is the
sparsity of worker answers (i.e., each worker only answers a small number of
tasks). Simple extension of the existing approaches (e.g., Laplace perturbation
and randomized response) may incur large error of truth inference on sparse
data. Thus we design an efficient new matrix factorization (MF) algorithm under
LDP. We prove that our MF algorithm can provide both LDP guarantee and small
error of truth inference, regardless of the sparsity of worker answers. We
perform extensive experiments on real-world and synthetic datasets, and
demonstrate that the MF algorithm performs better than the existing LDP
algorithms on sparse crowdsourcing data.
