Declustering the iTrust Search and Retrieval Network to Increase Trustworthiness

Declustering the iTrust Search and Retrieval Network to Increase Trustworthiness

Presentation by Christopher Badger

Research conducted in collaboration with Yung-Ting Chuang, Isai Michel Lombera, Louise E. Moser and P. M. Melliar-SmithUniversity of California, Santa Barbara

Supported in part by NSF Grant CNS 10-16103

Overview

Introduction

Design and Implementation of iTrust

Peer Neighborhoods

Declustering Algorithm

Clustering Coefficients

Results and Analysis

Expectation of Cooperation

Conclusions and Future Work

WEBIST 2012 iTrust Christopher Badger

IntroductionSearch engines such as Google and Yahoo! have played

an increasingly important role in today's world They offer fast and accurate search results … ideally

They are centralized, and therefore vulnerable to:

Attack Censorship

iTrust is our solution to this problem iTrust is a P2P network that functions by distributing metadata

about documents and search requests to random nodes in the iTrust membership

Designed to be resistant to censorship and attack


Design of iTrust

Source ofInformation



Requester ofInformation

RequestEncounters

Metadata

Design of iTrust



Requester ofInformation

RequestMatched

Design of iTrust


apachePHP

public interface

delete nodes

leave membership

query

search

inbox

statistics

user settings

tools

metadata inbox

tika / lucene / dictionary

metadata functionsmetadata xml engine

register metadata list

apply xml

publish xml list

helper functions

nodes wrapper

keywords wrapper

resource wrapper

tag keyword resource

search functions

globals / navigation

cURL

SQLite

session

log

PECL http

(a) (b) (c)

HTTP Implementation of iTrust


Neighborhoods

We define the neighborhood of a node as: all of the other nodes to which the node is directly connected

Importance of a node's neighborhood The flow of information

Neighborhoods cannot be unlimited in size Too expensive to track the entire network


Neighborhoods

Green nodes comprise peer A's neighborhood

A


Neighborhoods

Beneficial to have only trustworthy nodes in one's neighborhood

How to determine which nodes are trustworthy? How to define trustworthiness?

Randomness Why is a random neighborhood useful? How to achieve neighborhood randomness?




The process Ask all current neighbors for a list of their neighbors Create a master list containing all of these gathered lists Ensure the list contains only unique peers Drop all existing connections, effectively clearing the

neighborhood Select new neighbors randomly from the obtained list Can be done in a manner similar to the binomial distribution,

where each node has an equal chance to become a neighbor




What Declustering Does Randomizes each node's neighborhood

Reduces the clustering coefficient of the node performing declustering

The clustering coefficient is a measure of how cliquish the network is

Is performed locally by each node Does not require a global context Lowers the expectation of cooperation


Metrics Local clustering coefficient is defined as:

To calculate the local clustering coefficient of node X, put all of X's neighbors into a set S

Find E, the number of possible edges between all nodes in S

• For an undirected graph, this number is: Find e, the number of edges that exist between nodes

in S The local clustering coefficient for X is given as:

Global clustering coefficient is defined as: The average of all of the local clustering coefficients

(S )×((S )−1)2

eE

the number of edges between a node's neighborsthe number of edges that could occur between them


│S│x (│S│- 1)

Metrics Maximum degree of any node in the network

The number of connections of the most connected node in the network

Used as a reference for the prevalence of hubs in the network

Match probability The probability that an iTrust search in the particular graph results

in a hit

Network view The cardinality of the set containing all of a node's neighbors

and the neighbors of those neighbors

Average network view The average of all nodes' network views


Results

Erdős–Rényi Graph Watts-Strogatz GraphBarabási–Albert Graph


ResultsMaximum Hub Degree

Average Network View

Global Clustering Coefficient

Match Probability

Erdős–Rényi Graph

Initial1st Pass2nd Pass3rd Pass

192187187190

1000100010001000

0.15020.15010.15010.1499

0.92820.92830.92820.9279

Watts-Strogatz Graph


150187185180

301100010001000

0.74500.15060.15030.1501

0.28580.92860.92830.9290

Barabási–Albert Graph


492246187186

1000100010001000

0.23990.15330.15050.1508

0.96520.92970.92810.9283


Results


Results and Analysis Declustering reduced the clustering coefficient in both the

Watts-Strogatz and Barabási–Albert graphs

Declustering evened out the degree distribution in the network, acting to eliminate any hubs

For the Watts-Strogatz graph, the iTrust match probability greatly increased

Overall, declustering was able to effectively turn the Watts-Strogatz and Barabási–Albert graphs into random graphs similar to the Erdős–Rényi graph

By promoting network randomization, the minimum expectation of cooperation was decreased, thereby increasing robustness


Expectation of Cooperation

Definition: Subjectively, the degree to which nodes act or rely on

information provided by other nodes Minimum Expectation of Cooperation

The minimum degree of cooperation expected from all nodes in order for the network to function well

Importance A lower minimum expectation of cooperation allows

nodes in the network to continue functioning well, despite increased resistance or attack by others


Expectation Of Cooperation


Conclusions

The declustering strategy increases iTrust's trustworthiness by randomizing peer neighborhoods

Declustering also decreases the global clustering coefficient of the network, which helps improve message forwarding performance

iTrust can be valuable for people who seek information on the Internet and are wary of potential censorship


Future Work

We are looking into combining declustering and different message relaying strategies to increase network robustness

In addition to the HTTP implementation, we are also developing an SMS implementation for iTrust

We intend to release all iTrust source code and related documentation to the general public


Questions?

Our iTrust Web Sitehttp://itrust.ece.ucsb.edu

Contact InformationChristopher Badger: [email protected] Chuang: [email protected] Michel Lombera: [email protected]

Our project is supported by NSF CNS 10-16193


Documents

Declustering the iTrust Search and Retrieval Network to Increase Trustworthiness