Real-world trajectory sharing with local differential privacy

Abstract

Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping n -grams (i.e., contiguous subsequences of length n ) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories. Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.

GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy

“…Most work has focused on data publication with DP [e.g., 2,15,17,30,35,50,66], as opposed to data synthesis and LDP. Some other work exists on private trajectory synthesis and publication [e.g., 36,37,54], but recently proposed solutions, in both the centralized [e.g., 36,37] and local settings [19], all possess a common limitation. They all produce outputs that correspond to arbitrary grid cells or places of interest, whereas we generate coordinate data (i.e., the same form as the input data).…”

“…We note that there has been some work on location data in local settings (i.e., LDP and variants thereof). Chen et al [16] use personalized LDP for spatial data aggregation, Xiong et al [69] focus on continuous location sharing using randomized response, and Cunningham et al [19] publish LDP-compliant sequences of places of interest. However, extensions of these works to our setting are unviable owing to their fundamentally different problem and/or privacy settings.…”

“…Generating synthetic datasets of high utility is a fundamental challenge in many data management tasks, such as private data sharing [e.g., 18,19,28,48,74], benchmark generation [e.g., 31,34], and database management system testing [8,9,60].…”

A privacy preserving querying mechanism with high utility for electric vehicles

“…In [10], Cunningham et al studied the problem of trajectory sharing under DP and proposed a mechanism to tackle it. However, this work assumes the setting of an offline trajectory sharing which breaks down in the practical environment where the trajectories are being shared online as there is no prior information or limitation on the number of queries made by an EV during a journey, and their respective locations.…”

“…However, this work assumes the setting of an offline trajectory sharing which breaks down in the practical environment where the trajectories are being shared online as there is no prior information or limitation on the number of queries made by an EV during a journey, and their respective locations. Therefore, the method proposed by the authors in [10] cannot be directly adapted to our dynamic environment closely simulating the real-world scenario for such a use case.…”