SS-LDP: A Framework for Sparse Streaming Data Collection Based on Local Differential Privacy

Abstract

The continuous collection of streaming data in the Internet of Things (IoT) may compromise user privacy, as such data often originates from personal information. Local differential privacy (LDP) is a novel privacy notion that offers a strong privacy guarantee to all users without relying on a trusted data collector. However, existing LDP-based studies mainly focus on static scenarios or perturbation of data points at a single timestamp without sufficiently considering data sparsity, which adds excessive noise and leads to low utility. Therefore, we propose a Framework for Sparse Streaming Data Collection based on Local Differential Privacy (SS-LDP), which aims to provide high utility at each timestamp while satisfying $w$-event LDP. One component is the introduction of an upper-bound optimization mechanism, which reduces the noise scale by combining error minimization with the gradient descent method. Another component of SS-LDP targets the efficient management of privacy resources through two specific strategies. First, significant changes in streaming data are captured by calculating differences between the latest few data points, thereby conserving the privacy budget. Second, an improved sparse privacy budget allocation mechanism quantifies data sparsity at each timestamp using the moving average method, enabling efficient allocation of the privacy budget for each timestamp. SS-LDP is evaluated using two real-world datasets and compared with four baseline methods that satisfy $w$-event privacy. Extensive experiments and theoretical analyses are conducted to demonstrate the superiority of our framework.