Time-Efficient Geo-Obfuscation to Protect Worker Location Privacy over Road Networks in Spatial Crowdsourcing

Abstract

To promote cost-effective task assignment in Spatial Crowdsourcing (SC), workers are required to report their location to servers, which raises serious privacy concerns. As a solution, geo-obfuscation has been widely used to protect the location privacy of SC workers, where workers are allowed to report perturbed location instead of the true location. Yet, most existing geo-obfuscation methods consider workers' mobility on a 2 dimensional (2D) plane, wherein workers can move in arbitrary directions. Unfortunately, 2D-based geo-obfuscation is likely to generate high traveling cost for task assignment over roads, as it cannot accurately estimate the traveling costs distortion caused by location obfuscation. In this paper, we tackle the SC worker location privacy problem over road networks. Considering the network-constrained mobility features of workers, we describe workers' mobility by a weighted directed graph, which considers the dynamic traffic condition and road network topology. Based on the graph model, we design a geo-obfuscation (GO) function for workers to maximize the workers' overall location privacy without compromising the task assignment efficiency. We formulate the problem of deriving the optimal GO function as a linear programming (LP) problem. By using the angular block structure of the LP's constraint matrix, we apply Dantzig-Wolfe decomposition to improve the time-efficiency of the GO function generation. Our experimental results in the real-trace driven simulation and the realworld experiment demonstrate the effectiveness of our approach in terms of both privacy and task assignment efficiency. CCS CONCEPTS • Security and privacy → Security services; • Theory of computation → Mathematical optimization; Parallel algorithms.

A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy

“…NDSS 2017[Yu et al(2017], ICDCS 2019 [Qiu and Squicciarini(2019)], CIKM [Qiu et al(2020)], TMC 2022 ], SIGSPATIAL 2022 ], UAI 2022[Imola et al(2022], EDBT 2023[Pappachan et al(2023].…”

“…[ Chatzikokolakis et al(2015)]). While some recent efforts [Qiu et al(2020), ] have managed to extend the size of secret datasets to around 300 records using Dantzig-Wolfe decomposition and column generation algorithms, their focus has been on specific LP formulations where the mDP constraints are imposed for all pairs of secret records, simplifying the initialization of column generation. This specific LP formulation, however, is hard to apply in general mDP problems where the constraints are required only for neighboring records.…”