1. Learning Routing Paths in Anonymous Wireless Protocols Yu Jin Nishith Pathak

2. Wireless Anonymity System Goal: –To hide the communication paths between the peers Applications: –E-Voting –Military applications Characteristics: –Lack of centralized infrastructure –Wireless medium (broadcasting)

3. Wireless Anonymity Protocols ANODR (UCLA, ACM MOBIHOC 2003) –Encrypted message, no covert traffic, fixed routing paths. AnonDSR (SASN 2005) –Enhancement of ANODR, covert traffic Are they secure?

4. Objectives Break famous wireless anonymity protocols by predicting the edges Analyze the relations between anonymity, message rate and covert traffic rate Design a better wireless anonymity system.

5. Problem Definition MANET: Assumptions: –Messages are encrypted. –Routing paths are predefined and fixed. –At time t i, a sender v k sends out a message to the receiver with probability p 0. –If v m is the next hop on the routing path, then p(v m,t+1 |v k,t )=1. –All the nodes except the senders will randomly broadcast with probability p 1 in each round. –The senders could also broadcast covert traffic.

6. Example We have limited information by passively monitoring each node. (p 0 =0.2, p 1 =0.2)

7. Methodology Basic Idea: If two nodes broadcast at consecutive time intervals then there is a chance that they are consecutive hops on some path in the network Determine –P ab = P(a t =1,b t+1 =1 or b t =1,a t+1 =1 ) i.e. probability that a and b broadcast at two consecutive time intervals from observed data Fit P ab for all pairs of nodes (a,b) into a mixture of two Gaussians Pairs of nodes with lower probabilities will be grouped under one Gaussian and pairs of nodes with higher probabilities will be grouped into the second Gaussian Pairs of nodes in the second Gaussian are taken as edges lying on some path in the network –Using these edges we can construct the network routing paths

8. Methodology EM-algorithm was used to fit a mixture of two Gaussians on – –P ab for all pairs of nodes (a,b) –Logit(P ab ) for all pairs of nodes (a,b) Alternative approach: Mixture of two multi-variate Gaussians was fit on vectors V ab = [P 11 P 01 P 10 P 00 ] for all pairs of nodes (a,b) –P 11 = P ab –P 01 = P(a t =0,b t+1 =1 or b t =0,a t+1 =1) –P 10 = P(a t =1,b t+1 =1 or b t =1,a t+1 =0) –P 00 = P(a t =0,b t+1 =0 or b t =0,a t+1 =0)

9. Simulation Settings

10. Scenarios Changing the number of observations. Changing covert traffic rate Changing message rates. Prediction rate when senders will send out both message and covert traffic.

11. Results Changing message rate

12. Results (2) Changing number of iterations

13. Result (3) Covert traffic rate changes, fixed

14. Result (4) Covert traffic rate changes, randomized

15. Result (5) Covert traffic rate changes, arbitrary

16. Result (6) Senders also broadcast randomly

17. Future Work Incorporate knowledge of network topology into the model Consider the effects of changing topology and increasing communication paths How to predict edges when senders broadcast randomly More complex simulation scenarios