Effective truth discovery under local differential privacy by leveraging noise-aware probabilistic estimation and fusion

Abstract

Truth discovery is an effective way to eliminate data inconsistency by integrating different worker-provided values. Although directly conducting non-private truth discovery approaches based on uploaded noisy values after adding Laplace noise for continuous inputs guarantees rigorous local differential privacy (LDP), it may result in poor performance due to the lot of contained noise. First, the injected noise for privacy protection randomly sampled from Laplace distribution may be excessive even with a large privacy budget, as the above distribution is unbounded and drops sharply with respect to the x-axis. Built-in Gaussian noise also usually exists within these uploaded noisy values, which may also have a negative effect on the aggregated truths under LDP and makes the problem investigated in this paper far more challenging. In this paper, we focus on obtaining accurate truths in the above cases under rigorous LDP for continuous inputs, and present a novel solution TESLA. The key idea of this solution is that we let injected noise for privacy protection and inherent Gaussian noise only weakly negatively affect the weight estimation and true aggregation. In particular, we design a runtime filtering mechanism (RFM) to obtain the supremum and infimum for the values after adding Laplace noise by considering these two types of noise together. Moreover, we develop a probabilistic fusion mechanism (PFM) to get the fused values by adaptively using the obtained supremum and infimum. Furthermore, we devise a probabilistic weight mechanism (PWM) to obtain a more accurate weight for each worker. Therefore, truth discovery can be conducted based on the new weight of each worker and the filtered values. We provide theoretical analyses of TESLA’s utility, privacy and complexity. Experimental results demonstrate the effectiveness and efficiency of TESLA. We also extend and verify TESLA over typical mean estimation as well as standard deviation calculation, and various machine learning tasks (e.g., logistic regression, support vector machine (SVM) and neural network). Experimental results also demonstrate its superiority.