Stacey Greenaway Managing Reputation and Trust in Peer-to-Peer Networks. CP4022 Research Topics in...

Stacey Greenaway

Managing Reputation and Trust in Peer-to-Peer Networks.

CP4022 Research Topics in Networks and Distributed Systems.

Assessment 2

Stacey Greenaway 0487622

Introduction

Definitions

Research 1 - XRep

Research 2 – Bayesian Network

Research 3 - Trust Vectors

Research 4 – EigenTrust

Problems

Conclusions

Introduction

Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

What is a peer–to-peer network?A decentralized network

All nodes in the network act as both clients and servers

Powered by the bandwidth of all peers

Ad hoc connections

Types of P2P Network:Filesharing e.g. Gnutella, Kazaa, BitTorrent

Instant Messaging, Ecommerce

Exisitng Trust and Reputation systems:Ebay Feedback System

Introduction


Trust - A peer’s trust in other peers based on his own past experience.

Reputation - A peer’s trust in another peer based on the experiences of other peers.

File Provider – a peer providing a file for download

Servent – a peer who is both client and server.

Free Rider - A peer who only downloads and does not share any files.

Inauthentic files – viruses, corrupt, unreadable, wrong file type, content not what it claimed to be.

Definitions


These threats provide the requirements for a trust and reputation system.

Decoy files - A malicious peer will respond to any query with a copy of the requested file, but will deliver a file that has been tampered with or contains a virus at the point of download.

Malicious peer - A peer who either belongs to one of the groups below or will provide an inauthentic file for every request.

Malicious collective - A group of malicious peers who know each other and collaborate to subvert a P2P system.

Self Replication – virus such as Gnutella vbs.worm poses as a peer and then creates a copy of itself for download.

Pseudospoofing - malicious peers control multiple identities, false pseudonyms are used to give good reputation to other pseudonyms controlled by the same malicious peer.

Definitions – Attacks and Threats


A basic explanation of a Trust and Reputation system:

Peers store opinions on their experiences at downloading files.

They store an opinion about the file provider and the file.

These opinions are computed either into binary or using another mathematical probability.

Peers share their opinions providing recommendations for file providers and files.

A peers opinion can be weighted based on how much the querying peer trusts them.

The aim of the system is to eliminate malicious peers and inauthentic files.

A Trust and Reputation System


A Reputation Based Approach for Choosing Reliable Resources inPeer-to-Peer Networks (Damiani et al.) [1]

Cited by the other 3 research papers. [2] [3] [4]

Propose a protocol called XRep:

A peer, p, queries the network for other peer’s opinions (votes) on resources and servents.

Resource repository - records an ID for each file downloaded and whether it is good(+) or bad(-)

Servent repository - stores the number of successful and unsuccessful downloads by each peer.

Votes are converted to binary, where a positive (+) = 1 and negative (-) = 0.

Research 1 - XRep


A Reputation Based Approach for Choosing Reliable Resources in Peer-to-Peer Networks (Damiani et al.) [1]

XRep has six phases: Resource Searching and Resource Selection:

query network, retrieving list of files, select one to download based on trust and reputation. Vote Polling:

peer (p) asks the other peers opinions (poll request) about the resource (r) it is about to download or on the servent (s) offering the resource. Poll responses encrypted using a public key called “pkpoll” - contains the responding peers vote, IP Address and Port.

Vote Evaluation:

“pkpoll” decrypted. p clusters the votes, which allows it to detect those sharing the same IP address. (pseudospoofing)An average value of all votes in the cluster is calculated and returned to the querying peer (p). A random selection of “voters” from each cluster is contacted for confirmation of their vote using the IP and Port encypted in “pkpoll”.

Best Servent Check:

Choose the most reputable servent to download file from.

Resource Downloading:After download, p updates his repositories with his opinion of both the servent and resource.

Research 1 - XRep


Trust and Reputation Model in Peer-to-Peer Networks (Wang et al.) [2]

Propose a Trust and Reputation Model using Bayesian Networks to build a profile of

each peer’s opinions based on different contexts of trust.

An Analogy:Mike has two friends John who is a mechanic and Bob who is a Doctor. Miketrusts Bob with a medical complaint but not to fix his car and respectively, trusts John to fix his car but not to diagnose a medical condition. So in the context of fixing a car John is trustworthy, but Bob is untrustworthy. What one peer may consider a good file is not what another peer would considergood. For instance peer A’s priority in a good file is its content regardless of its quality.

Research 2 - Bayesian Network



Bayesian network - A Bayesian Network is a graph consistingof nodes and arcs. Nodes represent variables and the arcs represent the relationships and dependencies between thevariables. ([5])

Fig 1 - basic Bayesian network consisting only of one parent and several child nodes.

FP - file provider

T - % of all positive interactions. Trust is dependant on Download Speed (DS), FileQuality (FQ) and File Type (FT).

More user preferences can be added to the Bayesian Network, e.g. copyright.

Trust in a condition only calculated once.



Fig 1 – Naïve Bayesian Network


The Trust and Reputation Model:

A peers reliability is measured as a file provider and a referee.

Queries are issued about the reliability of a file and its provider.

A Bayesian Network contains a peers opinions on all past interactions

Bayesian Networks are exchanged and compared to form groups of trusted peers. (The similarity of nodes is calculated, nodes with higher value (between 0 and 1) indicate

peer preferences)

The recommendations of trusted peers are weighted more heavily than unknown peers as they share similar preferences.

After every interaction the BNs of the file provider and the referees will be updated to reflect the peers trust in them.

Combat pseudospoofing or malicious collective attacks.

(false recommendation values will be obvious when compared to trusted peers and counteracted)




Experiments:

Simulated a file sharing system based on Gnutella.Each node is either a file provider or peer at one time.Each peer only knows its direct neighbour and a few fileproviders.

1000 interactions between 40 peers and 10 file providers.Each of ten runs is evaluated by taking averages of the results.

4 systems compared:Trust and Reputation with BNTrust and Reputation without BNTrust with BN Trust without BN

results: Systems where opinions are exchanged perform better.Using a BN only gives marginal increase in performance.



A Reputation-Based Trust Management System for P2P Networks

(Selcuk et al.) [3]

Propose a protocol to control the amount of inauthentic files a malicious peer

can disperse through the P2P network.

• “Trust Vectors” are kept locally by peers• Consult own “trust vector”, or request a “trust rating” from other peers• Uses query messages to gain recommendations from other peers• “Trust Vectors” are updated after every download with + or – opinion.



A Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) [3]

Trust Vectors

are binary consist of 8, 16, or 32 bitslength is stored as an integer variablepositive (1) or negative (0) opinion is represented in the vector as 1 bitupdates recorded at the vectors most significant bit

Trust Rating Calculated by dividing the sum of the Trust Vector by the power of 2, then dividing the result by 2 to the power of the number of significant bits in the vector.

Distrust RatingHas more weight than a Trust RatingMalicious action hard to recover from



Fig 2 Trust Vector

Fig 3 Trust Rating


The trust ratings of file providers are evaluated:

an average of the trust values of the most trusted peers in the list is calculated

determined by a set threshold value.

If No. Trusted Peers < Threshold a random selection of peers are queried

trust and distrust ratings will be included in the responses.

credibility rating gives weight to the opinions.

credibility vector - peer’s opinion truthful (1) untruthful (0)

Threshold sets number of responses to evaluate.


Managing Reputation and Trust in Peer- to- Peer Networks. Stacey Greenaway

Fig 3 Trust Evaluation


Experiments:

Test performance under various malicious attacks:

naïve

hypocritical

malicious collective

pseudospoofing

Simulation Spec:1000 peers and 1000 files

between 1% and 10% malicious

peer linked to 3 neighbours, query submitted over these links for 3 hops, specified by TTL





- represent’s the ratio of

malicious to all downloads.

Fig 4 Results


The EigenTrust Algorithm for Reputation Management in P2P Networks

(Kamvar et al.) [4] “to decrease the number of inauthentic files in a P2P file sharing system that assigns

each peer a unique global trust value based on the peers history of uploads”. [5]

Eigenvectors – A special set of vectors associated with Linear Algebra, and matrixes, where

left eigenvector is a row of the matrix and right eigenvector is a column of the matrix. ([6] [7])

Malicious peers identified rather than the inauthentic files Decoy Files

Self Replicating Worms

Malicious Collectives

Malicious Spies

Pseudospoofing

Research 4 - EigenTrust


The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Basic Trust Model

“Peer i is more likely to trust the opinions of peers from whom he has had an honest interaction

with in the past. ”. [5]

Local Trust Value - calculated from peer i’s experiences downloading from other peer’s, j.

Global Trust Value - calculated from the local trust values assigned to peer i by peers j. based on their experiences downloading from i.

Each peer computes its own Global Trust Value and stores it locally.

Local Trust Values are normalized.

Peers share Trust Values.

Trust Values are weighted by the amount of trust peer i places in the referee.

Peer i asks the friends’ of friends for their recommendations until a view of entire network is achieved.

Eventually Global Trust Value will represent the trust the entire system holds in peer j.



The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Basic Trust Model

Pre Trusted Peers, P

Peers who established the system or first users

Their distribution across the network =

Their trust values are used to break up malicious collectives or when a referee is inactive.



Fig 5 Basic EigenTrust Algorithm

The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Secure Trust Model

Score Managers

peers who compute the trust value of another peer in the system

Assigned using Distributed Hash Table (DHT)

Set of Daughter Peers, Di

Distributed Hash Table

hash functions map a Unique ID for each peer (IP Address and TCP port) into points in a logical coordinate space.

coordinate space is partitioned over the network, every peer covers a region of that dynamic space.

the peer who covers the region where that ID is hashed becomes that peers score manager.



Fig 6 Can Hash Space

(cited by Kamvar et al. (13) [4])

The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Secure Trust Model

Score Managers - computing Global Trust

computes the Global Trust Values of its Daughters

holds an opinion vector

queried to find the trust worthiness of d, where

The score manager learns:

set of peers who download from d and their opinion of d

set of peers who d has downloaded from and its opinion of those interactions.



The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Secure EigenTrust Algorithm



Fig 7 Secure EigenTrust Algorithm

The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Experiments



Fig 8 Simulation Settings

The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4]

Experiments

Threat Model Aindividual malicious peers

Threat Model Bmalicious collectives

Amount of malicious peers is increased by 10% - max. 70%.

Results:Inauthentic files make up approximately 10% of the network compared to a maximum of over 90% in a network without the proposed trust model. Using values of Pre Trusted Peers has broken up the malicious collective.



Fig 9 Threat Model A

Fig 10 Threat Model B

The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4]

Experiments

Threat Model Cmalicious collectives with camouflage

malicious peers who will offer authentic files some of the time in order to gain higher trust values, to increase their chance at being selected as a download source.

Amount of authentic files uploaded is increased by 10% - max. 90%.

Results:The more authentic files a malicious peer provides, the more impact they have at providing inauthentic files.Too costly in terms of bandwidth and disk space.



Fig 11 Threat Model C

Performance Bottleneck

Main problem facing Trust and Reputation systems

Peer with highest reputation always selected as download source

No suitable solution proposed

Random selection

Priority queue


Fig 12 Performance Bottleneck [4]

Problems

None of the proposed systems have been implemented in ‘real world’

Hard to measure trust and reputation when measuring human opinion

No standards that define what trust and reputation is and how it should be measured

Policing Quality of Service in P2P networks is virtually impossible due to their decentralized nature.

Each individual peer is responsible for the quality of the content they provide only

Not suitable for large P2P networks, only tested on small simulations.

Performance of these systems not tested in terms of bandwidth.

Excessive messaging places too much strain on network.


Conclusions

[1] Damiani, E. di Vimercati, D. C. Paraboschi, S. Samarati, P. Violante, F. (2002) Reputation-based approach for choosing reliable resources in peer-to-peer networks, Proceedings of the 9th ACM Conference on Computer and Communications Security.

[2] Wang, Y. Vassileva, J. (2003) Trust and Reputation Model in Peer-to-Peer Networks, Proceedings of IEEE Conference on P2P Computing, Linkoeping, Sweden.

[3] A. A. Selcuk, E. Uzun, M. R. Pariente (2004), A Reputation-Based Trust Management System for P2P Networks, 4th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2004), Chicago, USA.

[4] Kamvar, S. D. Schlosser, M. T. and Garcia-Molina. H. (2003) The eigentrust algorithm for reputation

management in P2P networks, Proceedings of the Twelfth International World Wide Web Conference.

[5] Wikipedia, the free encyclopedia (no date) Bayesian Network [online]. [cited 14th Apr 2006]. http://en.wikipedia.org/wiki/Bayesian_Network

[6] Wikipedia, the free encyclopedia (no date) Eigenvector [online]. [cited 14th Apr 2006]. http://en.wikipedia.org/wiki/Eigenvector

[7] Eric W. Weisstein. "Eigenvector." (no date) MathWorld--A Wolfram Web Resource. [cited 14th Apr 2006]. <http://mathworld.wolfram.com/Eigenvector.html>


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