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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 2

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Peer-to-Peer Wireless Networks 3 1 1 Message Identifier 2 2

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Examples 4 Urban Sensing networks Delay tolerant networks Peer-to-peer file exchange VANETs Social networks

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Location Privacy Problem 5 a b c Monitor identifiers used in peer-to-peer communications

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bluetoothtracking.org 6

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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 7 Message Identifier [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 Zones Prevent long term tracking 8 Mix zone 1 1 2 2 1 1 2 2 1 1 a a b 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 Zones Mix network Mix networks vs Mix zones 9 Mix node Mix node Mix node Mix node Mix node Mix node Alice Bob Alice home Alice work

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Where to place mix zones? 10

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Outline 1.Mix Zone Effectiveness 2.Placement of Mix Zones 3.Application Example 11 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 12 [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 13

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Trajectory 14 3/41/40 1/3 2/301/3 1/21/4

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Sojourn Time 15 ΔtΔt Pr( Δ t)

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Mix Zone Effectiveness Event-Based Metric [6] 16 P v is probability of assignment I = total number of assignments T t t Entering events Exiting events 1 2 ab

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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) 17 + + – –

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Mix Zone Effectiveness Flow-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 18

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Decision Theory Model Assume 2 flows f 1, f 2 converge to same exit 19 Mix zone 1 1 x x 2 2 Choice under uncertainty Any event

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Bayes Decision Rule Choose hypothesis with largest a posteriori probability Minimizes probability of error 20 is the a priori probability that an event belongs to f j is the conditional probability of observing x knowing that x belongs to f j

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pepe Probability of Error 21

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Jensen-Shannon Divergence Measure distance between probability distributions 22 Provides both lower and upper bounds for the probability of error

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Outline 23 Illustration of Metric

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Outline 1.Mix Zone Effectiveness 2.Placement of Mix Zones 3.Application Example 24

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Description Central authority decides offline where to deploy mix zones – Knows mobility model – Knows effectiveness of possible mix zones locations 25

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Distance to Confusion [9] Between mix zones, adversary can track nodes Mix zone = confusion point Bound distance between mix zones 26 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 27

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Placement Optimization Use a subset of all possible mix zones 28 Cost Distance to confusion Mix zone effectiveness where w i is cost of a mix zone W max is maximum cost C max is maximum distance-to-confusion

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Illustration of Algorithm 29 3 3 2 2 1 1 4 4

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Outline 1.Mix Zone Effectiveness 2.Placement of Mix Zones 3.Application Example 30

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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 31

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Map of New York City 32

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Metric & Configuration Matching success of mix zone i Tracking success System parameters – dtc <= 2km – cost <= 3 mix zones 33

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Mix Zone Performance 34

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Mix Zone Placement 35 (avg=0.48) (avg=1.56) (avg=1.55) (avg=3.56)

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Tracking Success for different deployments 36

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Performance of Deployment 37

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Tracking Success with different traffic conditions 38

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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 39 julien.freudiger@epfl.ch

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BACKUP SLIDES 40

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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 41

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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 42

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Event-based Metric Assume adversary knows mobility profiles Consider nodes entering/exiting mix zone i over T time steps P v is probability of assignment I = total number of assignments Average entropy: 43

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Generalization Consider average behavior 44 Mix zone 1 1 x x 2 2 2 2 2 2 2 2 1 1

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Mix Zone Placement 45 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 46

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Tracking Success for different mix zone radius 47

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Average Tracking Success 48

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