1. Presented By Siddartha Ailuri Graduate Student, EECS 04/07/17
Track Me If You Can: On the Effectiveness of Context-based Identifier Changes in Deployed Mobile Networks
Laurent Bindschaedler, Murtuza Jadliwala, Igor Bilogrevic, Imad Aad, Philip Ginzboorg, Valtteri Niemi, and Jean-Pierre Hubaux
Presented By
Siddartha Ailuri
Graduate Student, EECS
04/07/17

2. Agenda Introduction Mobile Network Model Adversary Model
Pseudonym Change Algorithm (PCA)
Data Collection & attack
Tracking Framework & Strategies
Empirical Evaluations
Conclusion

3. Introduction
Mix-Zones were proposed by Beresford et. al. in which the identifiers are changed with pseudonyms.
Context based identifier change algorithms.
In this paper the authors are conducting an experiment involving 80 volunteers for 4 months.
Mixed zones are dynamically created when a certain conditions are met like a certain number of devices in the neighborhood. When a mixed zone is created all devices in the zone will disconnect from the network and join the network with new pseudo MAC Address after the silent period. This will create confusion among the attacker by de-correlating the identities with traces.

4. Mobile Network Model
Mobile network testbed in EPFL campus

5. Cont.
The participants are expected to use Nokia N900 devices which was NIC enabled.
All devices are honest and the adversary has no direct access to devices.
Non-interactive and Interactive communications.
Adaptive Beaconing mechanism for neighbor discovery
Devices run Pseudonym change algorithm(PCA) which changes the pseudonym assigned to MAC address of the device.

6. Adversary Model
37 Access Points acting as sniffers across six interconnected buildings which run a “tcpdump” process to capture messages.
The adversary is interested in the location and identity in the message not the contents.
The adversary cannot replay the messages.

7. Pseudonym Change Algorithm (PCA)
Since the IP address is dynamical we concentrate our efforts in changing the MAC address.
PCA Evaluation is done in 3 sets of parameters Cost-effective, Intermediate and Privacy sensitive.

8. Cont.

9. Data Collection & attack
The attacker has collected the messages now he needs to reconstruct the path or location trace.
The dump contains multiple copies of data and the attacker needs to derive the position by using RSSI and position of sniffers.
The adversary will aggregate the paths and tries to construct the path close to the groundtruth.

10. Cont. Time-stamp Synchronization. Event identification
Synchronizing the time across all APs.
Event identification
Messages are recorded as events
Timestamp Aggregation
Creating an accurate timestamp for messages
Coordinate Mapping
Position estimation

11. Cont.

12. Tracking Framework & Strategies
Common Sniffing Stations
Speed Matching
Tracking Strategies
Locally Optimal Walk
Globally Optimal Walk

13. Empirical Evaluation Privacy Metrics Traceability Metric τ
Uncertainty Metric u
Traceability Uncertainty Metric µ
Clustering Metric c

14. Cont.

15. Cont.

16. Cont.

17. Cont.

18. Cont.

19. Conclusion
Outcomes
By changing the identifiers aggressively and randomly improves the privacy of user.
The silent period should be random rather than fixed.
New adversary model is needed.