track me if you can · state of the art • there have been a few studies on the effectiveness of...
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TRACK ME IF YOU CAN
Laurent Bindschaedler*
School of Communicat ions and
Computer Sciences, EPFL, Switzer land
Co-Authors:
Murtuza Jadl iwala* , Wichi ta State Univers i ty , USA
Igor Bi logrev ic , LCA EPFL
Imad Aad, Nokia Research Center
Phi l ip Ginzboorg, Nokia Research Center
Val t ter i Niemi , Nokia Research Center
Jean-Pier re Hubaux, LCA EPFL
On the Effectiveness of Context-based Identifier Changes
in Deployed Mobile Networks
* Equally contributing authors. Murtuza was affiliated with EPFL when this work was done.
Network and Distributed System Security Symposium (NDSS)
6 February 2012
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PERVASIVE SOCIAL NETWORKS
4/19/2012
Laurent Bindschaedler, IC EPFL 2
Accident at turn 1
Any one has extra ticket
Office-goers
Workers
Tourists
Bluetooth
WiFi P2P
WiFi P2P
3G/4G
3G/4G
3G/4G
1G 2G
I have one
A
A
B
B
Message Src Dst
C
C
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PRIVACY THREATS
• Most current devices have static identifiers, which allow service
providers or malicious parties to track the location of users
• Protecting the location privacy of users is critical
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Imad
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PRIVACY PROTECTION
• Commonly deployed scheme to preserve privacy: replace device
identifiers by short-lived identifiers or pseudonyms
• Mix zones are spatio-temporal regions where pseudonyms of
several users are changed (mixed) to provide decorrelation
between pseudonyms and devices [BeresfordS2003]
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Tarzan Superman Batman Imad
[BeresfordS2003] Beresford, A.R. and Stajano, F., Location privacy in pervasive computing in IEEE Pervasive Computing, 2003.
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STATE OF THE ART
• There have been a few studies on the effectiveness of mix zones and optimal placement [ButtyanHV2007,GerlachG2007, WiedersheimMKP2010, FreudigerSH2009, JadliwalaBH2011]
• A majority of these studies focuses on other network models such as Vehicular Ad-hoc NETworks (VANETs)
• Difficult to transfer to PerSoNs because human and social factors
• Due to the difficulty in running large-scale trials, many studies rely on simulated data
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[ButtyanHV2007] L. Buttyán, T. Holczer, and I. Vajda. On the effectiveness of changing pseudonyms to provide location privacy in VANETs. In ESAS, 2007.
[GerlachG2007] M. Gerlach and F. Guttler. Privacy in VANETs using changing pseudonyms - ideal and real. In IEEE VTC-Spring, 2007.
[WiedersheimMKP2010] B.Wiedersheim, Z. Ma, F. Kargl, and P. Papadimitratos. Privacy in Inter-Vehicular Networks: Why simple pseudonym change is not enough.
In IEEE/IFIP WONS, 2010.
[FreudigerSH2009] J. Freudiger, R. Shokri, and J.-P. Hubaux. On the optimal placement of mix zones. In PETS, 2009.
[JadliwalaBH2011] M. Jadliwala, I. Bilogrevic, and J.P. Hubaux. Optimizing mixing in pervasive networks: a graph-theoretic perspective. In Computer Security--
ESORICS 2011.
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TRACK ME IF YOU CAN
On the Effectiveness
of Context-based Identifier Changes
in Deployed
Mobile Networks
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PRESENTATION OUTLINE
1. System Model
2. Data Collection and Processing
3. Tracking Framework and Algorithms
4. Empirical Results and Evaluation
5. Conclusion
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PRESENTATION OUTLINE
1. System Model
2. Data Collection and Processing
3. Tracking Framework and Algorithms
4. Empirical Results and Evaluation
5. Conclusion
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SYSTEM MODEL
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t p u c π m =
m: message
t: timestamp
p: position
u: user
π: pseudonym
c: content
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NIC / NIC TRIAL
• Nokia Instant Community (NIC)
• Multi-hop peer-to-peer network based on IEEE 802.11
• Publish-subscribe messaging pattern
• Users organized into communities
• NIC Trial in a nutshell
• EPFL campus
• March to June 2011
• 80 participants (students and teachers)
• Nokia N900 smartphones with NIC preinstalled
• Log everything: usage, activity, message content, etc.
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PSEUDONYM CHANGE
ALGORITHM (PCA)
Alice
Bob
MIX!
Charles
Eve 4/19/2012
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PCA PRINCIPLES
• Change pseudonym
• based on context
• at fixed (randomized) intervals
• When a pseudonym change decision is made, the device
broadcasts a mix request and changes its pseudonym
• Upon receiving a mix request, other devices in the neighborhood
also change pseudonyms with some probability
• A quota is placed on the number of allowed pseudonym changes
to prevent network performance collapse
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PCA PARAMETERS
Cost effective Intermediate Privacy sensitive
Forced timer 14400s 7200s 3600s
Context timer 3600s 1200s 300s
Change threshold 7200s 1800s 600s
Neighbor threshold 1 2 3
Daily change quota 5 20 50
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PCA Strength
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ATTACKER MODEL
• Passive, eavesdrops using static mesh network of sniffing stations
• Weaker than the standard Dolev-Yao model
• Reconstruction attack
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100m
200m
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PRESENTATION OUTLINE
1. System Model
2. Data Collection and Processing
3. Tracking Framework and Algorithms
4. Empirical Results and Evaluation
5. Conclusion
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DATA PROCESSING
• Database insertion
• Groundtruth reconstruction
• Timestamp synchronization
• Event identification
• Coordinate mapping
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Attacker Experimenter
Step performed by
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PRESENTATION OUTLINE
1. System Model
2. Data Collection and Processing
3. Tracking Framework and Algorithms
4. Empirical Results and Evaluation
5. Future work
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TRACKING MODEL
• Finite-state first order Markov chain
• States S
• (pseudonym, first event → last event)
• Transition probabilities P : S x S → [0,1]
• 𝑃 𝑠𝑖 , 𝑠𝑗 = 1 ∀𝑠𝑖 ∈ 𝑆𝑠𝑗∈𝑆
• 𝑃 𝑠𝑖 , 𝑠𝑗 = 0 ∀𝑠𝑖 , 𝑠𝑗 𝑤𝑖𝑡ℎ 𝑡𝑒𝑛𝑑 𝑠𝑖 < 𝑡𝑠𝑡𝑎𝑟𝑡(𝑠𝑗)
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TRACKING ATTACKS
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TARGET TRACKING
• Single-Target Tracking (STT) is the tracking of a single
user in the state space
• Multiple-Target Tracking (MTT) is the simultaneous
tracking of several users in the state space
• MTT can sometimes be more accurate than STT because it
has a more global picture
• e.g., MTT can avoid collisions
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SEARCH STRATEGY
• L-WALK builds a walk in the state space such that the next
state candidate with the highest probability is selected at
every step
• such a walk is locally optimal
• G-WALK builds a walk in the state space such that the
probability over the entire walk is maximized over all
walks
• such a walk is globally optimal
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HEURISTICS TO ESTIMATE
TRANSITION PROBABILITIES
• Common sniffing stations
• «The more sniffing stations in common
between the current state and the next state
candidate, the more likely the candidate»
• Speed matching
• «The closer the user speeds between the
current state and the next state candidate,
the more likely the candidate»
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PRESENTATION OUTLINE
1. System Model
2. Data Collection and Processing
3. Tracking Framework and Algorithms
4. Empirical Results and Evaluation
5. Conclusion
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PRIVACY METRICS
• Traceability τ-metrics: Capture the extent to which the user can be tracked in time or distance [HohGXA2007]
• Uncertainty u-metrics: Capture the uncertainty in the next choice of pseudonym [DiazSCP2002]
• Traceability-uncertainty μ-metrics: Capture the extent to which the user can be tracked along with the difficulty (uncertainty) in the tracking (homebrewed)
• Clustering c-metrics: Capture the extent to which one user was confused for another [HohG2005]
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[HohGXA2007] B. Hoh, M. Gruteser, H. Xiong, and A. Alrabady. Preserving privacy in gps traces via uncertainty-aware path cloaking in ACM CCS, 2007.
[DiazSCP2002] C. Diaz, S. Seys, J. Claessens, and B. Preneel.Towards measuring anonymity in PET, 2002.
[HohG2005] B. Hoh and M. Gruteser. Protecting location privacy through path confusion in SECURECOMM, 2005.
[ShokriFJH2009] Shokri, R. and Freudiger, J. and Jadliwala, M. and Hubaux, J.P., A Distortion-based Metric for Location Privacy in Proceedings of the 8th ACM
workshop on Privacy in the electronic society, 2009.
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GENERAL TRACKING RESULTS
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Direct application of tracking algorithms to the original data set
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TRACKING RESULTS
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Single-user tracking results
(averaged over the three sets of PCA parameters)
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TRACKING RESULTS
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Multi-user tracking results
(averaged over the three sets of PCA parameters)
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TRACKING WITH VARYING
ADVERSARY STRENGHTS
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Vary the number of sniffing stations by iteratively removing 6
stations (uniformly selected)
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TRACKING RESULTS
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Single-user tracking results
with varying adversary strength
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TRACKING RESULTS
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Multi-user tracking results
with varying adversary strength
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TRACKING IN LARGE USER
CLUSTERS
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Evaluation of the tracking effectiveness when user density is the highest
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TRACKING RESULTS
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Traceability in large user clusters
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TRACKING WITH
AGGRESSIVE PCA
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Simulate PCA with more aggressive parameters based on the
original data set
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AGGRESSIVE PCA PARAMETERS
Privacy sensitive Aggressive Very aggressive
Forced timer 3600s 1200 sec 600 sec
Context timer 300s 120 sec 60 sec
Change threshold 600s 300 sec 120 sec
Neighbor threshold 3 3 3
Daily change quota 50 200 500
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TRACKING RESULTS
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Single-user traceability results with more aggressive PCA
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TRACKING RESULTS
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Multi-user traceability results with more aggressive PCA
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TRACKING WITH
IMPROVED PCA
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Simulate modified versions of the PCA on the original data set
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PCA IMPROVEMENTS
• PCA-2
• Randomize radio silence over a larger time interval
• PCA-3
• Observe longer radio silence periods
• PCA-4
• Maintain radio silence until there has been significant
movement of the user
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TRACKING RESULTS
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Multi-user traceability results with improved PCA
(obtained using common sniffing stations heuristic)
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TRACKING RESULTS
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Multi-user traceability results with improved PCA
(obtained using speed matching heuristic)
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PRESENTATION OUTLINE
1. System Model
2. Data Collection and Processing
3. Tracking Framework and Algorithms
4. Empirical Results and Evaluation
5. Conclusion
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CONCLUSION
• Even basic tracking strategies can achieve high success
in real life
• Pseudonym change has an impact on network
performance which should not be neglected when
designing an algorithm and choosing parameters
• tradeoff is required
• Standard pseudonym change algorithms should be
modified to improve protection
• e.g., by taking movement into consideration
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WANNA PLAY WITH THE NIC
TRIAL DATA?
Here’s where you can find more info:
https://lausanne.nokiaresearch.com/nic
or
http://bit.ly/nictrial
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REFERENCES
[Gartner2009] A Lapkin. Context-aware computing: Four questions CIOs should be asking. Gartner, 2009.
[BeresfordS2003] Beresford, A.R. and Stajano, F., Location privacy in pervasive computing in IEEE Pervasive Computing, 2003.
[ButtyanHV2007] L. Buttyán, T. Holczer, and I. Vajda. On the effectiveness of changing pseudonyms to provide location privacy in VANETs. In ESAS, 2007.
[GerlachG2007] M. Gerlach and F. Guttler. Privacy in VANETs using changing pseudonyms - ideal and real. In IEEE VTC-Spring, 2007.
[WiedersheimMKP2010] B.Wiedersheim, Z. Ma, F. Kargl, and P. Papadimitratos. Privacy in Inter-Vehicular Networks: Why simple pseudonym change is not enough. In IEEE/IFIP WONS, 2010.
[FreudigerSH2009] J. Freudiger, R. Shokri, and J.-P. Hubaux. On the optimal placement of mix zones. In PETS, 2009.
[HohGXA2007] B. Hoh, M. Gruteser, H. Xiong, and A. Alrabady. Preserving privacy in gps traces via uncertainty-aware path cloaking in ACM CCS, 2007.
[DiazSCP2002] C. Diaz, S. Seys, J. Claessens, and B. Preneel.Towards measuring anonymity in PET, 2002.
[HohG2005] B. Hoh and M. Gruteser. Protecting location privacy through path confusion in SECURECOMM, 2005.
[ShokriFJH2009] Shokri, R. and Freudiger, J. and Jadliwala, M. and Hubaux, J.P., A Distortion-based Metric for Location Privacy in Proceedings of the 8th ACM workshop on Privacy in the electronic society, 2009.
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