Human Mobility and Location Privacy in Wireless Sensor Networks by Ryan Andrew Vogt A thesis ...

119 Pages·2014·2.63 MB·English

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
(cid:13)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)protectstheprivacyoftheuser’slocationofinterest, butfacilitates
the adversary determining the user’s current location. We propose a formal method for
measuringhowwellissuingk queriestolocationsdispersedthroughoutthenetworkprotects
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,thereisadirecttrade-offbetweentheuser’slocation-of-interestprivacyandhis/her
current-location privacy, controlled by the value of k the user chooses.
User interactions with sensor networks do not occur in featureless, uniform environ-
ments. 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 mo-
bility 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, con-
strainedbytheirsurroundingsinanoutdoorurbanenvironment. Resultsobtainedfromour
publiclyavailableDestination-BasedSpaceSyntaxSimulator(DBS3),independentfromour
k-queryexperiments, demonstratewhichmobilitymodelparametersaffectwirelessnetwork
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
ii
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.
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Preface
Chapter 2 is based on a previously published paper: Ryan Vogt, Ioanis Nikolaidis, and
Pawel Gburzynski. A realistic outdoor urban pedestrian mobility model. Simulation Mod-
elling Practice and Theory, 26:113–134, 2012. Ryan Vogt was responsible for the design of
themobilitysimulationsystemdescribedtherein,theimplementationofthatsystem,experi-
mentaldesign,analysis,andmanuscriptcomposition. IoanisNikolaidisandPawelGburzyn-
ski were supervisory authors, and were involved with concept formation and manuscript
composition.
Chapter 3 is a significant extension of a previously published paper: Ryan Vogt,
Mario Nascimento, and Janelle Harms. On the trade-off between user-location privacy
and queried-location privacy in wireless sensor networks. In Proceedings of the 8th Interna-
tional Conference on Ad-Hoc Networks and Wireless, pages 241–254, 2009. Ryan Vogt was
responsibleforthedesignandimplementationoftheexperiments, analysis, andmanuscript
composition. Mario Nascimento and Janelle Harms were supervisory authors, and were
involved with concept formation and manuscript composition.
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Dedication
To my beautiful and brilliant wife, Stefanie.
v
Acknowledgements
Thank you to the Natural Sciences and Engineering Research Council of Canada, and
iCORE and Alberta Advanced Education & Technology for funding this research in-part.
Thank you also to Ioanis Nikolaidis, Mario Nascimento, and Pawel Gburzynski for super-
vising me through the arduous journey that was this degree.
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Contents
1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Key Contributions to Mobility Simulation . . . . . . . . . . . . . . . . . . . . 2
1.3 Key Contributions to Network Privacy . . . . . . . . . . . . . . . . . . . . . . 4
2 Human Mobility Simulation 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Mobility Model Design and Implementation . . . . . . . . . . . . . . . . . . . 11
2.3.1 Agents and Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 Destination Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.3 Pathfinding Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.4 Simulation Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4 Mobility Parameters and Wireless Networking Simulations . . . . . . . . . . . 34
2.5 Verification of DBS3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.6 Using DBS3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3 The Trade-Off Between Location-of-Interest and Current-Location Pri-
vacy 48
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.3 Adversary Model and Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 51
3.4 Privacy Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.4.1 Privacy of the Current Location and Location of Interest . . . . . . . 52
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3.4.2 Privacy of the Area of Interest . . . . . . . . . . . . . . . . . . . . . . 67
3.5 Choosing the Fake LOIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.6 The Partition and Partitioning Problems. . . . . . . . . . . . . . . . . . . . . 75
3.6.1 NP-Completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.6.2 Greedy Partition Approximation . . . . . . . . . . . . . . . . . . . . . 78
3.6.3 Divide-and-Conquer Partitioning . . . . . . . . . . . . . . . . . . . . . 80
3.7 Experimental Results in a Uniform Environment . . . . . . . . . . . . . . . . 84
3.8 Experimental Results with Mobility . . . . . . . . . . . . . . . . . . . . . . . 91
3.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4 Conclusions 98
Bibliography 100
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List of Figures
2.1 A map of downtown Edmonton, Canada from Google Maps [19], and the
associated street outlines used as input to DBS3. . . . . . . . . . . . . . . . . 12
2.2 A map of Fira, Greece from Google Maps [19], and the associated street
outlines used as input to DBS3. . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Two potential paths from a source to a destination, labelled with the angle
changes incurred during each change in direction. . . . . . . . . . . . . . . . . 19
2.4 Twopotentialpathstothedestination(thestar)convergingonasharedinter-
mediate point (the left circle). The upper path has both a smaller acquired
cost of θ <θ+(cid:15) and a smaller estimated final cost (assuming E estimates
the angle change required to reach the destination by using a straight line to
the destination). However, if (cid:15)<∆, the path with acquired cost θ+(cid:15) will
produce the minimal-angle path to the destination. . . . . . . . . . . . . . . . 21
2.5 Two examples in which a path h that can be reoriented to path h at a cost
1 2
of ∆, and h can be reoriented to the next intermediate point at a cost of δ. . 22
2
2.6 The new crumb, c , is on the same street as the old crumb, c , but
new old
in a different location on the street. The star is the destination, and the
angle change incurred by walking in a straight line to the destination is the
admissible heuristic, illustrated for c as e . . . . . . . . . . . . . . . . . . 30
old old
2.7 The effect of the centrality bias exponent, α, on the rates of information
spread and information collection in Edmonton. The distance decay expo-
nent, δ, was fixed at zero. Error bars represents 95% confidence intervals
around the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
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2.8 The effect of the centrality bias exponent, α, on the rates of information
spread and information collection in Fira. The distance decay exponent, δ,
was fixed at zero. Error bars represents 95% confidence intervals around the
mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.9 The effect of the distance decay exponent, δ, on the rates of information
spreadandinformationcollectioninEdmonton. Thecentralitybiasexponent,
α, was fixed at zero. Error bars represents 95% confidence intervals around
the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.10 The effect of the distance decay exponent, δ, on the rates of information
spread and information collection in Fira. The centrality bias exponent, α,
was fixed at zero. Error bars represents 95% confidence intervals around the
mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.11 The effect of the pathfinding algorithm on the rates of information spread
and information collection in Edmonton. The exponents α and δ were fixed
atzero. Errorbarsrepresents95%confidenceintervalsaroundthemeanover
2500 trials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.12 The effect of the pathfinding algorithm on the rates of information spread
andinformationcollectioninFira. Theexponentsαandδ werefixedatzero.
Error bars represents 95% confidence intervals around the mean over 2500
trials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.13 The effect of the pathfinding algorithm on the steady state distribution of
agents across ten fixed segments in Edmonton and Fira. The ten segments
chosen were those segments inhabited by the highest percentage of agents in
steady state when the minimizing-turns pathfinder was used. The exponents
α and δ were fixed at zero.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.14 Theeffectofthecentralitybiasexponent,α,andthedistancedecayexponent,
δ, onthecorrelationbetweenDBS3’ssteadystatedistributionofpedestrians
(usingtwodifferentpathfinders)andtheobserveddistributionofpedestrians
in Edmonton. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
x