1. Discovering the Most Trusted Agents Without Central Control
Tomasz Kaszuba
Krzysztof Rządca
Adam Wierzbicki
Polish-Japanese Institute of Information Technology
Warsaw, Poland

2. Discovering the Most Trusted Agents Without Central Control
Plan of presentation
Basic assumptions
Problem
Centralized vs Distributed approach
Goal
Trust Management Algorithms
Simple Algorithm
Adversary Models
Secure Algorithm
Distributed Sorting Algorithms
Experiments

3. Basic assumptions: Agents rely only on local information
Each agent is connected to d-other agents (neighborhood).
Agents communicate only with their neighborhood.
Standard Trust Management System that is capable of calculating objective trust present in the network.
for ex. EigenTrust(Kamvar et al., FuzzyTrust (Song et al.), GossipTrust and PowerTrust (Zhou and Hwang) B C A D E

4. Problem: Context Examples:
How to to select a certain subset of agents from the entire population, basing on their trustworthiness in a certain context.
Context Examples:
Replicate information in a distributed system
Select agents who are similar or more trusted.
Form the superpeer network from the Ad-hoc network
Select 10% of most trusted agents.

5. Centralized approach Distributed approach Advantages:
Full information about all peers
No possibility to cheat
Drawbacks:
Not efficient (churn, high computation cost, high message overhead)
Single point of failure
Vulnerable to bottlenecks
Distributed approach
Advantages:
Small computational cost for one peer
Denial of Service resistant
Drawbacks:
Vulnerable to various attacks

6. Simple Algorithm each agent ni assign the random number ri
each agent swaps its ri with nodes
from its neighborhood according to the trust value
swap occurs if
tiC<tjC and ri>rj or tiC>tjC and ri<rj
T=4
r=9
T=3
r=1
T=1
r=4
T=1
r=5
T=5
r=6
T=4
r=9
T=3
r=1
T=1
r=4
T=1
r=5
T=5
r=9
T=4
r=6
T=3
r=4
T=1
r=1

7. Simple Algorithm
after some steps order of ri gradually starts to reflect the order of the trust.
Nodes with r=9 and r=6 are most trusted!
T=1
r=4
T=5
r=9
T=4
r=6
T=3
r=5
T=1
r=1

8. OUR GOAL: ensuring the fair selection of trusted agents
in the presence of adversaries

9. Adversary models Choose value for initial ri in a non-random manner.
If many adversaries choose the same random number, the protocol will not be able to sort by exchanging these numbers.
T=1
r=5
T=5
r=5
T=4
r=5
T=3
r=5
T=1
r=5

10. Adversary models rc = F(3,4,8) ? 3 4 ? ? 8
Choose value for initial ri in a non-random manner.
Control the way in which initial ri are generated. A node computes its ri using secret sharing.
After computes the random number ri pre-shared secrets are kept as proofs.
rc = F(3,4,8) ? 3 A C A ? 4 C B B ? D 8 D E E

11. Adversary models
Cheat in exchanges of random numbers by claiming higher trust Ti (or lower ri).
It is possible to summon an arbiter (or more) who calculates the trust values for both parties and returnes a verdict.
Arbiters - control the fairness of an agent.
Can be selected from all agents in random manner.
T=5
r=9
T=5
r=6
My r=2 !
T=4
r=6
T=4
r=9
Set r=9 !
Set r=6 ! A

12. Adversary models PKI cryptography is used to sign messages.
Cheat in exchanges of random numbers by altering ri
in between exchanges or at the end of sorting by announcing false ri .
Undetectable without using the cryptography.
PKI cryptography is used to sign messages.
False ri can be easily detected in the next swap because it has wrong signature (it cannot be proven by adversary)

13. Fairness control
Frequency of such control depends on the reputation of an agent.
If the agent's reputation is below a treshold swap operations will be checked more frequently.
If agent attempts to cheat during the swap,
a negative report in the context of sorting fairness will be passed to the TM service.

14. Secure Algorithm:
Random number generation is assigned by the group of agents (whith secret sharing method)
Each agent swaps its ri with nodes from its neighborhood according to the trust value. Call the arbiter if required.
tiC<tjC and ri>rj or tiC>tjC and ri<rj
Swap operations are signed by both peers (and arbiter if required) and kept as proofs

15. Distributed
Sorting Algorithms

16. Sorting Algorithms - Ants
Swap messages are presented by ant-like objects.
Ants are passed from peer to peer using overlay network routing.
Ant returns to the requesting peer (the creator) using exactly the same path it used in the forward walk (ants leave the trails). A C A C B B D D E E

17. Sorting Algorithms - Ants
We design several types of ants:
RWxAnt - A standard random walk with range x.
SFxAnt - Sniffing First with x sniffing steps
NSxAnt – no stranger with range x
RWNRxAnt – random walk no return with range x
SFNSxAnt – sniffing first no strangers x sniffing steps
NSNRxAnt – no stranger No return with range x
TeleportAnt - Swap requests are performed between random peers in the network, without considering neighborhoods

18. Sorting Algorithms - Ants
RWxAnt - A standard random walk with range x.
RWNRxAnt – random walk no return with range x C
SWAP C C B B B
SWAP A A A D D D E E E
SWAP

19. Sorting Algorithms - Ants
SFxAnt - Sniffing First with x sniffing steps B B B A A A C C C D D D E E
SWAP E
D is best candidate

20. Experiments

21. Experiments pi = rank of node ni taken from random values
Network Size N = 10 to peers
Network Degree d = 2,3,4,5,7,10 connections per peer
Probablility distributions of Trust tic Pareto, Uniform
Churn factor: 0% and 5% per iteration (stable churn)
Experiment consists of 50 rounds (iterations).
Each experiment was repeated 10 times.
Quality measure:
pi = rank of node ni taken from random values
i = proper rank of node ni

22. Effect of distribution of trust value

23. Impact of churn

24. Future work
Different quality metric which can detect existence of adversaries in the system
New sorting algorithms
Protocol design and PlanetLab tests

25. http://utrust.pjwstk.edu.pl More information about
universal trust project
contact: