Download powerpoint presentation - Managing

Managing Reputation and Trust in
Peer-to-Peer Networks.
CP4022 Research Topics in Networks and Distributed Systems.
Assessment 2
Stacey Greenaway 0487622
Stacey Greenaway
Introduction
Introduction
Definitions
Research 1 - XRep
Research 2 – Bayesian Network
Research 3 - Trust Vectors
Research 4 – EigenTrust
Problems
Conclusions
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Introduction
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
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Definitions
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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Definitions – Attacks and Threats
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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
A Trust and Reputation System
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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 1 - XRep
A Reputation Based Approach for Choosing Reliable Resources in
Peer-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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 2 - Bayesian Network
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. Mike
trusts 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 consider
good. For instance peer A’s priority in a good file is its content regardless of its
quality.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 2 - Bayesian Network
Trust and Reputation Model in Peer-to-Peer Networks (Wang et al.) [2]
Bayesian network - A Bayesian Network is a graph consisting
of nodes and arcs. Nodes represent variables and the arcs
represent the relationships and dependencies between the
variables. ([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), File
Quality (FQ) and File Type (FT).
Fig 1 – Naïve Bayesian Network
More user preferences can be added to the Bayesian
Network, e.g. copyright.
Trust in a condition only calculated once.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 2 - Bayesian Network
Trust and Reputation Model in Peer-to-Peer Networks (Wang et al.) [2]
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)
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 2 - Bayesian Network
Trust and Reputation Model in Peer-to-Peer Networks (Wang et al.) [2]
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 file
providers.
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 BN
Trust and Reputation without BN
Trust with BN
Trust without BN
results:
Systems where opinions are exchanged perform better.
Using a BN only gives marginal increase in performance.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 3 - Trust Vectors
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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 3 - Trust Vectors
A Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) [3]
Trust Vectors
are binary
consist of 8, 16, or 32 bits
length is stored as an integer variable
positive (1) or negative (0) opinion is represented in
the vector as 1 bit
updates 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.
Fig 2 Trust Vector
Fig 3 Trust Rating
Distrust Rating
Has more weight than a Trust Rating
Malicious action hard to recover from
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 3 - Trust Vectors
A Reputation-Based Trust Management System for P2P Networks (Selcuk et
al.) [3]
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.
Fig 3 Trust Evaluation
Stacey Greenaway
Research 3 - Trust Vectors
A Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) [3]
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
Managing Reputation and Trust in Peer- to- Peer Networks.
Stacey Greenaway
Research 3 - Trust Vectors
A Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) [3]
F1 - represent’s
the ratio of
malicious to all
downloads.
Fig 4 Results
Managing Reputation and Trust in Peer- to- Peer Networks.
Stacey Greenaway
Research 4 - EigenTrust
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
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 4 - EigenTrust
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
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 4 - EigenTrust
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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Fig 6 Can Hash Space
(cited by Kamvar et al. (13) [4])
Stacey Greenaway
Research 4 - EigenTrust
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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 4 - EigenTrust
The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4]
Secure EigenTrust Algorithm
Fig 7 Secure EigenTrust Algorithm
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 4 - EigenTrust
The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4]
Experiments
Fig 8 Simulation Settings
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 4 - EigenTrust
The EigenTrust Algorithm for Reputation Management in P2P Networks
(Kamvar et al.) [4]
Experiments
Threat Model A
individual malicious peers
Threat Model B
malicious collectives
Amount of malicious peers is increased by 10% max. 70%.
Fig 9 Threat Model A
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 10 Threat Model B
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Research 4 - EigenTrust
The EigenTrust Algorithm for Reputation Management in P2P Networks
(Kamvar et al.) [4]
Experiments
Threat Model C
malicious 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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Fig 11 Threat Model C
Stacey Greenaway
Problems
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]
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
Conclusions
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.
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway
References
[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>
Managing Reputation and Trust in Peer-to-Peer Networks.
Stacey Greenaway