on the optimal placement of mix zones
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On the Optimal Placement of Mix Zones. Julien Freudiger , Reza Shokri and Jean-Pierre Hubaux PETS, 2009. Wireless Trends. Phones Always on (Bluetooth, WiFi ) Background apps New hardware going wireless Cars, passports, keys, …. Peer-to-Peer Wireless Networks. 1. 2. Identifier. - PowerPoint PPT PresentationTRANSCRIPT
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On the Optimal Placement of Mix Zones
Julien Freudiger, Reza Shokri and Jean-Pierre Hubaux
PETS, 2009
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• Phones– Always on (Bluetooth, WiFi)– Background apps
• New hardware going wireless– Cars, passports, keys, …
Wireless Trends
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Peer-to-Peer Wireless Networks
1
MessageIdentifier
2
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Examples
• Urban Sensing networks• Delay tolerant networks• Peer-to-peer file exchange
VANETs Social networks
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Location Privacy Problem
a
b
c
Monitor identifiers used in peer-to-peer communications
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bluetoothtracking.org
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Previous Work
• Pseudonymous location traces– Home/work location pairs are unique [1]
– Re-identification of traces through data analysis [2,3,5]
• Location traces without any pseudonyms– Re-identification of individual trace and home [4]
• Attack: Spatio-Temporal correlation of traces
MessageIdentifier
[1] P. Golle and K. Partridge. On the Anonymity of Home/Work Location Pairs. Pervasive Computing, 2009[2] A. Beresford and F. Stajano. Location Privacy in Pervasive Computing. IEEE Pervasive Computing, 2003[3] B. Hoh et al. Enhancing Security & Privacy in Traffic Monitoring Systems. Pervasive Computing, 2006[4] B. Hoh and M. Gruteser. Protecting location privacy through path confusion. SECURECOMM, 2005[5] J. Krumm. Inference Attacks on Location Tracks. Pervasive Computing, 2007
Pseudonym Message
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Location Privacy with Mix ZonesPrevent long term tracking
Mix zone
121
21
ab?
Change identifier in mix zones [6,7]• Key used to sign messages is changed• MAC address is changed
[6] A. Beresford and F. Stajano. Mix Zones: User Privacy in Location-aware Services. Pervasive Computing and Communications Workshop, 2004[7] M. Gruteser and D. Grunwald. Enhancing location privacy in wireless LAN through disposable interface identifiers: a quantitative analysis . Mobile Networks and Applications, 2005
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Mix ZonesMix network
Mix networks vs Mix zones
Mixnode
Mixnode
Mixnode
Alice Bob
Alice home
Alice work
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Where to place mix zones?
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Outline
1. Mix Zone Effectiveness
2. Placement of Mix Zones
3. Application Example
Shibuyu Crossing, Tokyo
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Mobility Model
• Nodes move according to flows [8]– A flow defines a trajectory in network– Nodes belong to a single flow– Several nodes share same flow
[8] M.C. Gonzalez, C.A. Hidalgo, and A.-L. Barabasi. Understanding individual Human Mobility Patterns. Nature, 2008
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Mix Zones Model
• Mix zones have – Set of entry/exit points– Traversed by mobile nodes
• Mobility profile of a mix zone [6]– Trajectory– Sojourn time
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Trajectory
3/41/4 0
1/31/3 1/32/30 1/31/21/4 1/4
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Sojourn Time
Δt
Pr(Δt)
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Mix Zone EffectivenessEvent-Based Metric [6]
Pv is probability of assignment I = total number of assignments
T
t
t
Entering events
Exiting events
21
( ) log ( )I
T v vv
i p pH
1 2
a b
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Event-Based Discussion
• Precise• Measures attacker success
• Requires installing eavesdropping stations at every mix zones
• What if nodes are across various windows T• High complexity (compute all assignments)
+
–
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Mix Zone EffectivenessFlow-based Metric
• Desired properties– Prior to network operation– Rely on general statistics of mobility– Efficient
• Key idea– Consider average behavior in mix zones– Measure probability of error of adversary
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Decision Theory Model
• Assume 2 flows f1, f2 converge to same exit
Mix zone
1
x2
Choice under uncertainty
Any event
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Bayes Decision Rule
• Choose hypothesis with largest a posteriori probability• Minimizes probability of error
is the a priori probability that an event belongs to fj
is the conditional probability of observing x knowing that x belongs to fj
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pe
Probability of Error
1 1( )p x 2 2 ( )p x
x
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Jensen-Shannon Divergence
• Measure distance between probability distributions
• Provides both lower and upper bounds for the probability of error
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OutlineIllustration of Metric
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Outline
1. Mix Zone Effectiveness
2. Placement of Mix Zones
3. Application Example
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Description
• Central authority decides offline where to deploy mix zones– Knows mobility model– Knows effectiveness of possible mix zones
locations
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Distance to Confusion [9]
• Between mix zones, adversary can track nodes• Mix zone = confusion point• Bound distance between mix zones
Mix zone 1
Mix zone 2
Distance-to-confusion
[9] B. Hoh et al.. Virtual Trip Lines for Distributed Privacy-Preserving Traffic Monitoring. MobiSys, 2008
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Cost of mix zones
• Use pseudonyms• Must remain silent for a period of time• Bound cost for each node
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Placement Optimization• Use a subset of all possible mix zones
Cost
Distance to confusion
Mix zone effectiveness
where wi is cost of a mix zone Wmax is maximum costCmax is maximum distance-to-confusion
{0,1}iz
ˆZ Z
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Illustration of Algorithm
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1
4
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Outline
1. Mix Zone Effectiveness
2. Placement of Mix Zones
3. Application Example
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Simulation Setup
• Urban mobility simulator (SUMO)– Real (cropped) map– Flows
• Attack Implementation (MOBIVACY)– Compute mobility profiles for each mix zone– Predict most probable assignment of
entering/exiting nodes for each mix zone
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Map of New York City
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Metric & Configuration• Matching success of mix zone i
• Tracking success
• System parameters– dtc <= 2km– cost <= 3 mix zones
Number of nodes matchedTotal number of nodesim
Number of nodes tracked over k consecutive mix zonesTotal number of nodes traversing k consecutive mix
(zones
)ts k
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Mix Zone Performance
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Mix Zone Placement
(avg=0.48)(avg=1.56)(avg=1.55)(avg=3.56)
0 1 2 3 4 5 6 7 80
10
20
30
40
50
60
70
80
BadRandomOptimalFull
Number of traversed mix zones
Frac
tion
of n
odes
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Tracking Success for different deployments
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Performance of Deployment
Bad Random Optimal Full0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
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Tracking Success with different traffic conditions
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Conclusion• Construct a network of mix zones• Measure of mix zones effectiveness based on
– Mobility profiles– Jensen-Shannon divergence
• Optimization model• Results
– Optimal algorithm prevents bad placement– 30% increase of location privacy compared to random
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BACKUP SLIDES
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Future Work
• Real mobility traces– More realistic intersection model
• Weight location in optimization– Some regions are more sensitive
• Larger map• Other attacks
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How to obtain mix zones?
• Silent mix zones– Turn off transceiver
• Passive mix zones– Where adversary is absent– Before connecting to Wireless Access Points
• Encrypt communications– With help of infrastructure– Distributed
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Event-based Metric
• Assume adversary knows mobility profiles• Consider nodes entering/exiting mix zone i
over T time steps
Pv is probability of assignment I = total number of assignments
• Average entropy:
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GeneralizationConsider average behavior
Mix zone
1
x
2222
1
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Mix Zone Placement
• Average number of traversed mix zone = average cost
• Optimal performs close to full at much lower cost
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Tracking Success for different adversary strength
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Tracking Success for different mix zone radius
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Average Tracking Success
Bad Random Optimal Full0
10
20
30
40
50
60
70
80
90
100
Frac
tion
of N
odes
Mat
ched