Truth Inference on Sparse Crowdsourcing Data with Local Differential Privacy

TL;DR

This paper proposes a novel matrix factorization algorithm that effectively infers true answers from sparse crowdsourcing data while ensuring local differential privacy, outperforming existing methods in accuracy.

Contribution

The paper introduces a new LDP-compatible matrix factorization method that handles answer sparsity and improves truth inference accuracy in privacy-preserving crowdsourcing.

Findings

The MF algorithm achieves high accuracy in truth inference under LDP.

The method outperforms existing LDP algorithms on real-world and synthetic datasets.

The approach maintains privacy guarantees regardless of data sparsity.

Abstract

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.