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• W3097567325 abstract "The evolution of smart devices and sensor-enabled vehicles has brought forward the capability of collecting large and rich datasets. The datasets provide unprecedented opportunities for devising the next generation of location-based decision systems. Analysing detailed continually updated information of a user's status such as location, speed and direction is vital in improving the safety, reliability, mobility and efficiency of any form of location-based services in smart cities. More generally, trajectory data is paramount for studying people's movement patterns, shopping behaviour and preferences (i.e., visited cafes, parks, and their sequence of points of interest). However, such fine-grained data raises significant concerns about the privacy of individuals, which in turn hinders the further development of next generation applications that benefit from trajectory data. Such data can reveal various sensitive information about individuals such as their home and workplace locations, whereabouts over time and health. Recent approaches to address such concerns use a strong privacy guarantee -- known as differential privacy. Their aim is to tackle a core privacy challenge: publishing modified datasets of individuals without compromising their privacy while not sacrificing the utility of the published data. However, the current approaches guaranteeing differential privacy are limited in scalability and utility for real applications which both are crucial for later usage or data analytics. In this thesis, we are concerned with publishing trajectory data which poses privacy risks due to its sequential nature. A key issue is that the known algorithms fail to preserve the utility of published trajectory data when perturbing it to satisfy differential privacy. Critical information of trajectory datasets such as total travel distances and frequent location patterns in trajectories cannot be fully preserved by the existing differentially private algorithms. This thesis investigates three research issues. First, it is known that simple histograms, which is widely studied under differential privacy, are insufficient to capture aggregated information for spatial data. Our first work shows how to use instead spatial histograms to provide accurate distribution of traffic counts with differential privacy guarantee. Spatial histograms must satisfy sequential constraints (spatial) and naively applying differential privacy can destroy sequential constraints. Our proposed algorithm computes new information about trajectory counts without destroying spatial constraints and hence, improves the utility of published data.We further refine the algorithm to improve the utility of the published data by incorporating the traffic distribution. Intuitively, dense regions gain more information about the trajectory counts compared to sparse regions. Since the density of different regions might be uneven, we need to directly use trajectory densities to accurately compute information about the trajectory distribution in the regions for efficiently scaling the added noise to ensure differential privacy. Spatial histogram data has limitations in terms of spatial queries. For example, we cannot ask queries such as ``how many trajectories start from location A and end at location B?''. To address this limitation, in our third work, instead of using count information from trajectories as in spatial histograms we use actual trajectory data. We introduce a graphical model to capture accurate statistics about the movement behaviours in trajectories. Using this model, our algorithm privately generates synthetic trajectories such that the noise is optimally added to capture the movement direction of a trajectory. Our algorithm preserves both the spatial and temporal information of trajectories in the generated dataset, requires less memory and computation than competing approaches, and preserves the properties of original trajectory data in terms of travelled distance, movement patterns and locations of interest. Our extensive theoretical and experimental analysis shows the significant improvement in the utility of published data generated by our algorithms." @default.
• W3097567325 created "2020-11-09" @default.
• W3097567325 creator A5005042479 @default.
• W3097567325 date "2019-01-01" @default.
• W3097567325 modified "2023-09-27" @default.
• W3097567325 title "Privacy-Preserving Approaches to Analyzing Sensitive Trajectory Data" @default.
• W3097567325 hasPublicationYear "2019" @default.
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