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networks, human mobility, location privacy, security

Vogt, Ryan A

Supervisor and department: Nikolaidis, Ioanis Computing Science Nascimento, Mario Computing Science

Examining committee member and department: Ravi, S. S. Computer Science, University of Pittsburgh Ardakani, Masoud Electrical and Computer Engineering Harms, Janelle Computing Science Elmallah, Ehab Computing Science

Department: Department of Computing Science

Specialization:

Date accepted: 2014-05-26T13:43:40Z

Graduation date: 2014-11

Degree: Doctor of Philosophy

Degree level: Doctoral

Abstract: By eavesdropping on a user-s query in a sensor network, an adversary can deduce both the user-s current location and his-her location of interest. Issuing k queries instead of one our -k-query- scheme protects the privacy of the user-s location of interest, but facilitates the adversary determining the user-s current location. We propose a formal method for measuring how well issuing k queries to locations dispersed throughout the network protects the privacy of the user-s location of interest, as well as a quantitative measure of how much information the k queries leak about the user-s current location. Experiments reveal that how physically dispersed the k queries are has no meaningful effect on the user-s privacy. However, there is a direct trade-off between the user-s location-of-interest privacy and his-her current-location privacy, controlled by the value of k the user chooses.User interactions with sensor networks do not occur in featureless, uniform environments. To facilitate the study of our k-query scheme in a rich environment characterized by realistically mobile users, we developed a new generative mobility model to produce mobility data for simulated agents. Existing generative mobility models suffer from a number of limitations. Most significantly, existing models are not representative of actual human movement. Our new mobility model is based on state-of-the-art work in understanding pedestrian mobility patterns in urban areas, known as Space Syntax. Under our model, agents move in a meaningful fashion in terms of destination selection and pathfinding, constrained by their surroundings in an outdoor urban environment. Results obtained from our publicly available Destination-Based Space Syntax Simulator DBS3, independent from our k-query experiments, demonstrate which mobility model parameters affect wireless network simulations in general: the pathfinding metric in grid-based urban centres and centrality bias in other urban centres.We combined DBS3 with our k-query scheme in order to study how long in advance a user should issue the k queries if travelling from some current location to his-her location of interest. While the exact threshold depends on the urban environment and speed of the agents in question, the typical threshold is very low, e.g., 10 minutes when using k=3 in downtown Edmonton, Canada.

Language: English

DOI: doi:10.7939-R38P5VJ2M

Rights: Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.





Autor: Vogt, Ryan A

Fuente: https://era.library.ualberta.ca/


Introducción



Human Mobility and Location Privacy in Wireless Sensor Networks by Ryan Andrew Vogt A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computing Science University of Alberta c Ryan Andrew Vogt, 2014 Abstract By eavesdropping on a user’s query in a sensor network, an adversary can deduce both the user’s current location and his-her location of interest.
Issuing k queries instead of one (our “k-query” scheme) protects the privacy of the user’s location of interest, but facilitates the adversary determining the user’s current location.
We propose a formal method for measuring how well issuing k queries to locations dispersed throughout the network protects the privacy of the user’s location of interest, as well as a quantitative measure of how much information the k queries leak about the user’s current location.
Experiments reveal that how physically dispersed the k queries are has no meaningful effect on the user’s privacy. However, there is a direct trade-off between the user’s location-of-interest privacy and his-her current-location privacy, controlled by the value of k the user chooses. User interactions with sensor networks do not occur in featureless, uniform environments.
To facilitate the study of our k-query scheme in a rich environment characterized by realistically mobile users, we developed a new generative mobility model to produce mobility data for simulated agents.
Existing generative mobility models suffer from a number of limitations.
Most significantly, existing models are not representative of actual human movement.
Our new mobility model is based on state-of-the-art work in understanding pedestrian mobility patterns in urban areas, known as Space Syntax.
Under our model, agents move in a meaningful fashion in terms of destination selection and pathfinding, constrained by their surroundings in an outdoor urban environment.
Results obtained from our publicly...





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