On the Optimal Placement of Mix Zones Julien Freudiger, Reza Shokri and Jean-Pierre Hubaux PETS,...

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

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MessageIdentifier

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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

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21

a

b?

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 1/3

2/30 1/3

1/21/4 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

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( ) 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

x

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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 matched

Total number of nodesim

Number of nodes tracked over k consecutive mix zones

Total 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

randomjulien.freudiger@epfl.ch

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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