This 22 slide presentation discusses peer-to-peer file sharing with incentives. It covers security issues in decentralized P2P networks, incentives for cooperation when uploading files, and different incentive systems including monetary systems, reputation systems, and trust groups. It also discusses Sybil attacks and game theoretic models of P2P systems. The presentation is for a course on performance evaluation taught by Dr. Saleh Yosefi to student Amir Maboudi at Urmia University.

1. PEER-TO-PEER FILE SHARING WITH INCENTIVES
Instructor: Dr.Saleh Yosefi
Student: Amir Maboudi
maboudi.amir@yahoo.com
Urmia university
course: Performance Evaluation
PEER-TO-PEER FILE SHARING
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2. PEER-TO-PEER FILE SHARING WITH INCENTIVES
Discussion: Security issues
Incentives for cooperation when uploading
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3. Security issues
• Security expert Bruce Schneider is often quoted as claiming that “Security is a
process.”
• Centralized or decentralized security administration .
• There is a significant overload for performance in p2p.
Decentralized : Very hard to manage. Centralized : single point of failure.
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4. Security issues
• P2P applies at the several levels:
 Network level:
 try to break routing system
 Block access to information by impeding queries
 Partitioning the network
 Application Level:
 Attempt to corrupt or delete data stored in the system or in transit
 User Level:
 The users themselves can be the subject of attacks
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5. Security issues
• The querying process
 Misforwarding queries (or responses)
 Phony queries
• The downloading process
 Content pollution (Malwares, Spam)
 Worm spread through P2P file-sharing systems
 Erroneous information
 Discarding queries (or responses)
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6. Incentives for cooperation when uploading
• Monetary systems based on a global currency.
• Systems based on cumulative reputations and trust groups
• Rule-based systems with memoryless "tit-for-tat" transactions
– Typically involve chunk swapping rather than single-chunk (i.e., client-server)
transfers.
– The peer arrival rate has exponential growth .
– Dealing with free riders is important .
– The rule is “ tit – for - tat” .
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7. Cumulative reputation system
• Avoid interactions with nodes that do not behave.
• Information about an actor that can aid in the prediction of future
behavior .
• As transactions occur, these reputation states will change.
• It serves two complementary purposes:
 Guide a decision maker’s choice in selecting transaction partners
 It can act as an incentive for good behavior for those who fear acquiring bad
reputations.
• Consequences
 Bad reputation : punishment or reduced privileges and isolation.
 Good reputation : motivating factor for good behavior.
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8. Cumulative reputation system
• Definitions
• π j > 0 is the propensity to cooperate of peer j.
• Rij is the reputation of j from i’s point of view.
• All reputations are normalized at each node.
• Gj(π j , R̄ i) is the probability that j responds positively to i’s query.
• The response function has the following properties:
 G is nondecreasing in both arguments
 G(π, R̄) = 0 and π > 0 imply R̄ = 0
 G(π,R̄) ≤ π j for all R̄ ∈ [0, 1].
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9. Cumulative reputation system
• Mean value of Ri :
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10. Cumulative reputation system (Simulation)
D
C
B
A H
E F G
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11. Cumulative reputation system
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12. Cumulative reputation system (Theorem)
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13. Cumulative reputation system (Simulation)
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14. Cumulative reputation system (Simulation)
 Reputation of a specific
Mean reputation of a
node j from i’s
specific node jpoint of view
β = 0.15
β= 0.95
0.95
N= 100 Nodes
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15. Cumulative reputation system (Simulation)
Individual increases indicate successful transaction for which
j was provider.
Reduction in sample path occur upon successful transaction
for which node j was not involved.
As expected, reputation converge to the nodes’ propensity
to cooperate.
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16. Cumulative reputation system
• Sybil Attack
• It is named after the subject of
the book Sybil
• a fictional case study of a woman
with multiple personality disorder.
• The name was suggested in or
before 2002 by Brian Zill at
Microsoft Research.
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17. Cumulative reputation system
• Sybil Attack
 Also known as pseudospoofing,
 An attacker acquires multiple identifiers in the system to undermine
some function of the system.
 if a single faulty entity can present multiple identities it can control a
substantial fraction of the system.
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18. Cumulative reputation system (Sybil Attack)
A and B select the broadcasts advertisementmalicious node C to
Malicious node C non-existent positions of messages of invented
Nodes A and B want to send their data towards the Sink.
forward their messages. Node C (yellow nodes)
non-existent position of nodes overhear s them.
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19. Cumulative reputation system (Sybil Attack)
Another example for Sybil attack
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20. Cumulative reputation system (Sybil Attack)
• Preventing “Sybil attacks” :
One approach is to have a trusted agency certify
identities.
The system must ensure that distinct identities refer to
distinct entities.
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21. Trust groups
• Based on trust groups
• Lightweight message authentication
 In the presence of both lying and spoofing of reputation
referrals.
• Peer registration mechanisms
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22. Trust groups (Simulation)
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23. Trust groups
•One group’s reputation from the
point of view of another.
•Intra-group transactions were more
frequent than inter-groups.
•group reputation sample path
appears smoother and has a
shorter transient phase than the
•Depicts an reputation node’s path.
individual individual sample
mean reputation (within group)
•Decreases in the sample path occur
less frequently than in non-hierarchical
system counterpart because inter-group
transactions had no effect (i.e., a lower
transaction rate).
Reputations fluctuate about their expected mean cooperation value as in the
non-hierarchical experiments.
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24. Game-theoretic models of P2P systems
• Involve end users that behave in a rationally selfish
manner.
• Peers may modify their own "default" cooperation
level.
• Achieve a desired utility from the system .
• At the end of each round, peers evaluate their
success rate and adjust their uplink rate accordingly
to maximize their net utility
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25. References
1. An introduction to communication network analysis ,
George Kesidis, 2007 , Wiley.
2. Peer-to-Peer Security - Allan Friedman, Harvard University -
L Jean Camp, Harvard University
3. The Sybil Attack , John R. Douceur , Microsoft Research ,
johndo@microsoft.com
4. Cumulative Reputation Systems for Peer-to-Peer Content
Distribution ,B. Mortazavi and G. Kesidis,CS&E and EE Depts,The
Pennsylvania State University , University Park, PA, 16802 mortazav@cse.psu.edu
and kesidis@engr.psu.edu also a member of technical staff at Verizon Wireless
5. And some other Papers and websites like WikiPedia .
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26. Questions
Thank you for your consideration
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