Managing uncertainty in moving objects databases

Trajcevski, Goce; Wolfson, Ouri; Hinrichs, Klaus; Chamberlain, Sam

doi:10.1145/1016028.1016030

Cited by 279 publications

(213 citation statements)

References 27 publications

Supporting

Mentioning

208

Contrasting

Unclassified

Order By: Relevance

“…GPS data) available today and the interest in providing location-based services using such data, moving objects databases have received much attention in recent years [37,42,8]. We consider a moving objects scenario where a number of point objects are moving around in a 2-dimensional space.…”

Section: Dataset I: Moving Objectsmentioning

confidence: 99%

Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

Kanagal

Deshpande

2008

2008 IEEE 24th International Conference on Data Engineering

116

View full text Add to dashboard Cite

In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms commonly used to implement dynamic probabilistic models, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new readings arrive. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over sensor data from the Intel Lab dataset that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of such tight integration between dynamic probabilistic models and database systems.

show abstract

Section: Dataset I: Moving Objectsmentioning

confidence: 99%

Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

Kanagal

Deshpande

2008

2008 IEEE 24th International Conference on Data Engineering

116

View full text Add to dashboard Cite

show abstract

“…A well-known type of measurable query in trajectory analysis is spatiotemporal range queries, which were introduced by Trajcevski et al in [22] in 2004. In particular, we consider the two following queries.…”

Section: Trajectory Analysis and Utility Measuresmentioning

confidence: 99%

The Fréchet/Manhattan Distance and the Trajectory Anonymisation Problem

Torres

Trujillo-Rasúa

2016

Data and Applications Security and Privacy XXX

View full text Add to dashboard Cite

Abstract. Mobile communication has grown quickly in the last two decades. Connections can be wirelessly established from almost any habitable place in the earth, leading to a plethora of connection-based tracking mechanisms, such as GPS, GSM, RFID, etc. Trajectories representing the movement of people are consequently being gathered and analysed in a daily basis. However, a trajectory may contain sensitive and private information, which raises the problem of whether spatio-temporal data can be published in a private manner. In this article, we introduce a novel distance measure for trajectories that captures both aspect of the microaggregation process, namely clustering and obfuscation. Based on this distance measure we propose a trajectory anonymisation heuristic method ensuring that each trajectory is indistinguishable from k −1 other trajectories. The proposed distance measure is loosely based on the Fréchet distance, yet it can be computed efficiently in quadratic time complexity. Empirical studies on synthetic trajectories show that our anonymisation approach improves previous work in terms of utility without sacrificing privacy.

show abstract

“…In [44] six types of spatio-temporal range queries were introduced, aimed at evaluating the relative position of a moving object with respect to a region R in a time interval [t b , t e ]. We have used these queries in our experimental work, even though they were designed for use on uncertain trajectories (see Definition 4) rather than synthetic trajectories.…”

Section: Euclidean) and ω A Constant That Penalizes For Removed Lomentioning

confidence: 99%

“…Unlike in [44], our anonymized trajectories are not uncertain; hence, we will only use the two spatio-temporal range queries proposed in that paper that can be adapted to non-uncertain trajectories:…”

Section: Definition 5 (Possible Motion Curve) a Possible Motion Curvementioning

confidence: 99%

Microaggregation- and permutation-based anonymization of movement data

Domingo-Ferrer

Trujillo-Rasúa

2012

Information Sciences

View full text Add to dashboard Cite

Movement data, that is, trajectories of mobile objects, are automatically collected in huge quantities by technologies such as GPS, GSM or RFID, among others. Publishing and exploiting such data is essential to improve transportation, to understand the dynamics of the economy in a region, etc. However, there are obvious threats to the privacy of individuals if their trajectories are published in a way which allows re-identification of the individual behind a trajectory. We contribute to the literature on privacy-preserving publication of trajectories by presenting a distance measure for trajectories which naturally considers both spatial and temporal aspects of trajectories, is computable in polynomial time, and can cluster trajectories not defined over the same time span. Our distance measure can be naturally instantiated using other existing similarity measures for trajectories that are appropriate for anonymization purposes. Then, we propose two heuristics for trajectory anonymization which yield anonymized trajectories formed by fully accurate true original locations. The first heuristic is based on trajectory microaggregation using the above distance and on location permutation; it effectively achieves trajectory k-anonymity. The second heuristic is based only on location permutation; it gives up trajectory k-anonymity and aims at location k-diversity. The strong point of the second heuristic is that it takes into account reachability constraints when computing anonymized trajectories. Experimental results on a synthetic data set and a real-life data set are presented; for similar privacy protection levels and most reasonable parameter choices, our two methods offer better utility than comparable previous proposals in the literature.

show abstract

Managing uncertainty in moving objects databases

Cited by 279 publications

References 27 publications

Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

The Fréchet/Manhattan Distance and the Trajectory Anonymisation Problem

Microaggregation- and permutation-based anonymization of movement data

Contact Info

Product

Resources

About