A Gaussian Distribution-Based Truth Discovery Algorithm under Local Differential Privacy

Truth discovery is an effective tool for discovering the truth from a multitude of data points of varying quality, which inherently involves privacy concerns. While existing studies have predominantly focused on protecting workers’ submitted sensing data using local differential privacy (LDP), they overlook a crucial aspect of real-world scenarios: workers are likely to provide more accurate data for tasks they perceive as important, resulting in submissions that more closely approximate the truth for these tasks. Moreover, the prevalent use of the Laplace mechanism for noise addition, due to the inherent randomness and unboundedness of the Laplace distribution, might lead to excessive noise, potentially compromising the accuracy of truth discovery and yielding a noisy approximation of the truth. To address these limitations, we propose a Gaussian distribution-based truth discOvery approach under Local Differential privacy (GOLD). The algorithm’s core innovation lies in its comprehensive utilization of Gaussian distribution for both task importance and worker quality after adding Laplacian noise. Workers first apply Laplacian noise to their data locally, after which the problem is formalized as a constraint optimization task, deriving an iterative equation for the noisy truth. Theoretical analysis demonstrates that the GOLD algorithm rigorously adheres to local differential privacy requirements while achieving high truth accuracy and low time complexity. Empirical validation on two real datasets reveals that, compared to state-of-the-art algorithms, the GOLD algorithm improves the truth accuracy by at least 20%.