38,494 research outputs found
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
Context Trees: Augmenting Geospatial Trajectories with Context
Exposing latent knowledge in geospatial trajectories has the potential to
provide a better understanding of the movements of individuals and groups.
Motivated by such a desire, this work presents the context tree, a new
hierarchical data structure that summarises the context behind user actions in
a single model. We propose a method for context tree construction that augments
geospatial trajectories with land usage data to identify such contexts. Through
evaluation of the construction method and analysis of the properties of
generated context trees, we demonstrate the foundation for understanding and
modelling behaviour afforded. Summarising user contexts into a single data
structure gives easy access to information that would otherwise remain latent,
providing the basis for better understanding and predicting the actions and
behaviours of individuals and groups. Finally, we also present a method for
pruning context trees, for use in applications where it is desirable to reduce
the size of the tree while retaining useful information
Dynamic update of a virtual cell for programming and safe monitoring of an industrial robot
A hardware/software architecture for robot motion planning and on-line safe monitoring has been developed with the objective to assure high flexibility in production control, safety for workers and machinery, with user-friendly interface. The architecture, developed using Microsoft Robotics Developers Studio and implemented for a six-dof COMAU NS 12 robot, established a bidirectional communication between the robot controller and a virtual replica of the real robotic cell. The working space of the real robot can then be easily limited for safety reasons by inserting virtual objects (or sensors) in such a virtual environment. This paper investigates the possibility to achieve an automatic, dynamic update of the virtual cell by using a low cost depth sensor (i.e., a commercial Microsoft Kinect) to detect the presence of completely unknown objects, moving inside the real cell. The experimental tests show that the developed architecture is able to recognize variously shaped mobile objects inside the monitored area and let the robot stop before colliding with them, if the objects are not too small
A new splitting-based displacement prediction approach for location-based services
In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Several location prediction models have been proposed to enhance and increase the relevance of the information retrieved by users of mobile information systems, but none of them studied the relationship between accuracy rate of prediction and the performance of the model in terms of consuming resources and constraints of mobile devices. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. One such technique is the Prediction Location Model (PLM), which deals with inner cell structure. The PLM technique suffers from memory usage and poor accuracy. The main goal of this paper is to propose a new path prediction technique for Location-Based Services. The new approach is competitive and more efficient compared to PLM regarding measurements such as accuracy rate of location prediction and memory usage
SLIM : Scalable Linkage of Mobility Data
We present a scalable solution to link entities across mobility datasets using their spatio-temporal information. This is a fundamental problem in many applications such as linking user identities for security, understanding privacy limitations of location based services, or producing a unified dataset from multiple sources for urban planning. Such integrated datasets are also essential for service providers to optimise their services and improve business intelligence. In this paper, we first propose a mobility based representation and similarity computation for entities. An efficient matching process is then developed to identify the final linked pairs, with an automated mechanism to decide when to stop the linkage. We scale the process with a locality-sensitive hashing (LSH) based approach that significantly reduces candidate pairs for matching. To realize the effectiveness and efficiency of our techniques in practice, we introduce an algorithm called SLIM. In the experimental evaluation, SLIM outperforms the two existing state-of-the-art approaches in terms of precision and recall. Moreover, the LSH-based approach brings two to four orders of magnitude speedup
Representing, storing and mining moving objects data
Data about moving objects have been collected in huge amounts due to the proliferation of mobile devices, which
capture the position of objects over time. Studies about moving objects have been developed as a specific research area of
Geographic Information Systems. Those systems are designed to process traditional, static or slowly changing, geospatial
data. However, moving objects have inherent a dynamism that requires different approaches to data storage and analysis.
This paper presents a review of the key concepts associated to moving objects and their characteristics, as well as the
approaches proposed to store data about moving objects. For the analysis of moving objects, an overview of the existing data
mining techniques and some future guidelines are also presented
Emerging privacy challenges and approaches in CAV systems
The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions
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