PSH: A Private and Shared History-Based Incentive Mechanism.
ABSTRACT Fully decentralized peer-to-peer (P2P) systems do not have a central control mechanism. Thus, different forms of control mechanisms are required to deal with selfish peers. One type of selfish behavior is the consumption of resources without providing sufficient resources. Therefore, incentive schemes encourage peers to share resources while punishing selfish peers. A well-known example of an incentive scheme is Tit-for-Tat (TFT), as used in BitTorrent. With this scheme, a peer can only consume as much resources as it provides. TFT is resilient to collusion due to relying on private histories only. However, TFT can only be applied to peers with direct reciprocity. This paper presents a private and shared history (PSH) based incentive mechanism, which supports transitive relations (indirect reciprocity). Furthermore, it is resilient to collusion and it combines private and shared histories in an efficient manner. The PSH approach uses a shared history for identifying transitive relations. Those relations are verified using private histories. Simulations show that the PSH mechanism has a higher transaction success ratio than TFT.
- SourceAvailable from: citeseerx.ist.psu.edu
Conference Proceeding: A Taxonomy of Rational Attacks.[show abstract] [hide abstract]
ABSTRACT: For peer-to-peer services to be effective, par- ticipating nodes must cooperate, but in most scenarios a node represents a self-interested party and cooperation can neither be expected nor enforced. A reasonable assumption is that a large fraction of p2p nodes are rational and will attempt to maximize their consumption of system resources while minimizing the use of their own. If such behavior violates system policy then it constitutes an attack. In this paper we identify and create a taxonomy for rational attacks and then identify corresponding solutions if they exist. The most effective solutions directly incentivize c oop- erative behavior, but when this is not feasible the common alternative is to incentivize evidence of cooperation inst ead.Peer-to-Peer Systems IV, 4th International Workshop, IPTPS 2005, Ithaca, NY, USA, February 24-25, 2005, Revised Selected Papers; 01/2005
Conference Proceeding: The Sybil Attack.[show abstract] [hide abstract]
ABSTRACT: Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these "Sybil attacks" is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...Peer-to-Peer Systems, First International Workshop, IPTPS 2002, Cambridge, MA, USA, March 7-8, 2002, Revised Papers; 01/2002
- [show abstract] [hide abstract]
ABSTRACT: In this work we study the effectiveness of mechanisms for decentralized reputation management in P2P networks. We depart from EigenTrust (7), an algorithm designed for rep- utation management in file sharing applications over p2p networks. EigenTrust has been proved very effective against three different natural attacks from malicious coalitions while it performs poorly on particular attack organized by two dif- ferent kinds of malicious peers. We propose various metrics of reputation based on ideas recently introduced for detect- ing and demoting Web spam. We combine these metrics with the original EigenTrust approach suggested in (7). Our mechanisms are more effective than EigenTrust alone for detecting malicious peers and reducing the number of inau- thentic downloads not only for all the cases addressed in (7) but also for more sophisticated attacks.05/2007;
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PSH: A private and shared history-based incentive mechanism
Bocek, T; Kun, W; Hecht, F V; Hausheer, D; Stiller, B
Bocek, T; Kun, W; Hecht, F V; Hausheer, D; Stiller, B (2008). PSH: A private and shared history-based incentive
mechanism. In: Hausheer, D; Schönwälder, J. Resilient Networks and Services. Berlin, 15-27.
Postprint available at:
Posted at the Zurich Open Repository and Archive, University of Zurich.
Originally published at:
Hausheer, D; Schönwälder, J 2008. Resilient Networks and Services. Berlin, 15-27.
PSH: A Private and Shared History-based
Thomas Bocek1, Wang Kun2, Fabio Victora Hecht1, David Hausheer1, and
1Department of Informatics IFI, University of Zurich, Switzerland
2Research Institute of Telecommunication Transmission RITT, China
Telecommunication Technology Labs, China
3Computer Engineering and Networks Laboratory TIK, ETH Zurich, Switzerland
Abstract. Fully decentralized peer-to-peer (P2P) systems do not have
a central control mechanism. Thus, different forms of control mecha-
nisms are required to deal with selfish peers. One type of selfish behavior
is the consumption of resources without providing sufficient resources.
Therefore, incentive schemes encourage peers to share resources while
punishing selfish peers. A well-known example of an incentive scheme
is Tit-for-Tat (TFT), as used in BitTorrent. With this scheme, a peer
can only consume as much resources as it provides. TFT is resilient to
collusion due to relying on private histories only. However, TFT can only
be applied to peers with direct reciprocity.
This paper presents a private and shared history (PSH) based incentive
mechanism, which supports transitive relations (indirect reciprocity).
Furthermore, it is resilient to collusion and it combines private and shared
histories in an efficient manner. The PSH approach uses a shared history
for identifying transitive relations. Those relations are verified using pri-
vate histories. Simulations show that the PSH mechanism has a higher
transaction success ratio than TFT.
Peer-to-peer (P2P) systems have numerous advantages over centralized sys-
tems. Load balancing, robustness, scalability, and fault tolerance are properties
that a P2P system can offer. However, challenges in P2P systems include free-
riders , malicious peers, Sybil attacks , self-interest , and other forms
of attacks . Incentive mechanisms are used to address those challenges and
encourage peers to act cooperatively.
A simple incentive scheme is Tit-for-tat (TFT) as used in BitTorrent .
In this scheme, a peer can only consume as much resources as it provides. The
TFT mechanism keeps, for each peer, a history of past resource exchanges or
transactions. This history is private, that is, it contains only first-hand infor-
mation; consequently, peers have a limited view on transactions to peers with
direct reciprocity. Thus, private history is suitable for symmetric resource in-
terest , for example in a file-sharing system with many popular files. With a
TFT mechanism based on shared history, transaction information is shared and
accessible by other peers, with the result that indirect reciprocity is detectable.
Thus, TFT based on shared history (transitive TFT) is suitable for asymmetric
resource interest. However, shared history approaches are prone to false reports
and collusion .
This paper proposes a new incentive mechanism which is a combination of
private and shared history. While shared history is used to propagate resource
exchange information, private history is applied to verify its correctness. This
approach is termed private shared history incentive mechanism (PSH). Addi-
tionally, PSH uses an efficient mechanism to propagate history by including it
in resource request and response messages.
The evaluation of PSH requires two steps. First, to show that the mechanism
works as expected in general. Second, to test this mechanism in a distributed
environment such as PlanetLab. This paper presents the first step. PSH incentive
mechanism simulations show that this approach has a transaction success ratio of
up to 73% higher than TFT. Message count is, in average, 46% higher. Message
size is 288% larger, in average.
There are several possible applications of PSH. In file-sharing systems with
many unpopular files, there are few direct relations between peers for which TFT
does not work well. In this case, PSH shows its strength. Additionally, PSH can
be applied for trading resources such as computing power, memory capacity, or
bandwidth in a computational Grid. In such a Grid, it is rarely the case that
Grid nodes have symmetric resource interests, since those resources shared in a
Grid are typically non-replicable and exclusive, i.e. resource usage diminishes the
total amount of available resources . PSH enables a Grid node to contribute
resources to the Grid in idle times, and to consume additional resources from
other Grid nodes during peak times.
The remainder of this paper is structured as follows. Section 2 discusses
related work while Section 3 introduces the design of the incentive scheme. Sec-
tion 4 provides the implementation details and presents results, while Section 5
Incentive schemes can be divided into two groups: (1) trust-based and (2) trade-
based incentive schemes . With a trust-based incentive scheme, peers are
encouraged to act in a certain way to gain as much trust as possible. With a
trade-based incentive scheme, resources are traded and peers are encouraged
to provide as much resources as they consume. This can be based on direct
reciprocity as in TFT, or indirect reciprocity as in transitive TFT.
2.1Trust-based Incentive Schemes
Kamvar et al.  propose a global unique trust value based on a peer’s history.
The EigenTrust algorithm can effectively identify malicious peers and isolate
them. The authors have showed in simulations that their approach can reduce
the number of peers providing inauthentic files.
Lian et al.  propose multi-level TFT to achieve robust incentives. This
approach is a balance between EigenTrust and TFT. The authors have imple-
mented and tested their approach in the Maze  system. Multi-level TFT
introduces limited indirect trust levels, to reduce the trust metric size. As in
EigenTrust, multi-level TFT aggregates transitive trust values. The evaluation
shows that a two-level matrix performs better than relying on private history
only, while efficiently dealing with malicious peers.
Another enhancement of EigenTrust is described in . The authors com-
pared EigenTrust with the following different extensions: inverse EigenTrust,
truncated PageRank, bit propagation, and badness based on BadRank. Simula-
tions showed that EigenTrust with badness based on BadRank performs always
better than EigenTrust alone with respect to authentic downloads.
A similar approach to EigenTrust is described in . The authors propose a
voting reputation system, in which opinions of other peers are considered. Those
peers can vote on a peer’s reputation. A highly reputable peer vote has a higher
value. However, this approach is prone to whitewashing, and a high cost for
initial joining is a side effect.
PeerTrust  compares the trustworthiness of peers using a new trust metric.
This metric uses three important trust parameters: feedback, number of trans-
actions, and the credibility of the feedback. The authors showed in simulations
the feasibility of their approach.
2.2Trade-based Incentive Schemes
Trade-based incentive schemes, such as KARMA  and the mechanism used
by PPay  introduce a broker role. PeerMint  uses multiple remote peers to
store and aggregate accounting information in a trustworthy and scalable way.
It applies a structured P2P overlay network to map accounts onto a redundant
set of peers and organizes them in an efficient and scalable manner. The scheme
uses session mediation peers to maintain and aggregate session information about
transactions between peers. This minimizes the possibility of collusion. However,
malicious peers acting as a brokers or mediators are an open issue.
Feldman et al.  proposed to use the MaxFlow algorithm for collusion free
data propagation. The authors suggest to modify the MaxFlow algorithm to
evaluate paths in constant time. However, not all paths will be found (indirect
Ngan et al.  present an architecture to enforce fair sharing of storage
resources, which is robust against collusion. The architecture uses auditing and
usage records, which are publicly available. A peer can be randomly audited by
any other peer. The authors show in simulations that the overhead of auditing is
small and scales in large networks, and that peers have an incentive to provide
2.3Comparison of Incentive Schemes
Table 1 shows a comparison of related work. Since PeerMint, KARMA and PPay
require a broker, and PeerTrust focus on metrics, they are not included in this
table. Unlike multi-level TFT, PSH does not aggregate the propagated values.
PSH verifies each information directly on a peer, thus detecting collusion. The
path finding algorithm presented by Feldman et al. requires many contacts with
other peers, while PSH requires few contacts because transaction data is locally
available, as this data is propagated with each request. The work presented by
Ngan et al. uses auditing to verify resource information, while PSH tries to find
a transitive resource exchange path.
Table 1. Related work comparison
Feldman et al.
Ngan et al.
Aggregation Transitive exchange Collusion resistance Local data
PSH uses shared history to find peers with indirect reciprocity and private his-
tory to verify the reciprocity for those peers.
Although PSH uses shared history, the mechanism shall be collusion resistant.
In addition, data propagation must be scalable, robust, and fault tolerant. To
allow an initial transaction, a peer may consume resources until a credit limit is
reached. The credit limit must be low enough to discourage peers from creating
new identities (white-washing). A last requirement is the workload to be placed
on the requesting peer, preventing DoS attacks. If load is placed mostly on the
answering peer, too many requests may cause an overload.
It is assumed that peers have asymmetric resource interests, i.e. a peer that
provides resources to another peer has no interest in the resources that peer has
to offer. Furthermore, each peer’s public key is assumed to be known or able
to be requested. Since signatures are verified directly on the respective peer, no
trusted third party is required.
PSH behaves like TFT if two peers are about to exchange resources and pre-
vious transaction information is present in the other’s private history. If it is
not present, PSH looks for a path, that is, a linked list of peers with transitive
history information, from the source peer to the target peer. Each of those peers
in the path is requested to issue a check, that means, to transfer their signed
credit balance between the source and the next in the path or the target.
Figure 1 shows the general architecture of the request / response handling
in PSH. Arrows between grey boxes indicate a TCP or UDP connection. The
figure shows that the workload is mainly on the requesting peer, because path
searching is done on the requesting peer.
In Figure 1, peer s sends a resource request to peer t. If the request succeeds,
then both private histories from peer s and t are updated as in TFT. If it fails,
peer s searches for a path. If a path cannot be found, the request fails. If the
path cannot be traversed, the request fails as well. If the path can be traversed
and a valid check can be returned, peer s asks peer t again with this valid check.
Fig.1. General PSH incentive mechanism
3.4Shared History Data Propagation
Each peer stores two tables of history information, a direct transaction informa-
tion (DTI) table and an indirect transaction information (ITI) table. A DTI
table contains information based on direct reciprocity (private history). An ITI
table is based on indirect reciprocity (shared history). A DTI entry for peer x
and peer y DTIx(y) is defined as the amount of exchanged resources. For a suc-
cessful resource transaction from peer x to peer y, the former stores DTIx(y),
while peer y stores DTIy(x), where DTIx(y) = −DTIy(x). Along with each
request and response message, a subset of DTI entries with the highest times-
tamp is exchanged to avoid creating new connections. The ITI table contains
accumulated DTI from other peers, e.g., peer x has ITIy(z) = 3, which means
peer x knows about transactions between peer y and peer z. The ITI and DTI
tables are used to find a path from a given source to a target. If such a path
exists, then indirect reciprocity can be inferred.
Since many paths may exist, the size of a complete ITI table has polynomial
complexity O(n2), where n is the total number of peers in the system. A reduc-
tion of complexity can be achieved by evaluating a limited path length L instead
of |n|, where L < n. Further complexity reduction can be achieved by expiring
transactions in the ITI table using a time decay function fdecay(transaction).
Therefore, not all existing paths are found.
A transaction between two peers x and y is defined as Tx(y) = z, z being
the transaction value. Each transaction contains a timestamp. Figure 2 shows
example values of DTI and ITI in a state in which transactions T have already
happened. In this example, peer s is the source and peer t is the target. For
better readability, the ITI table is only displayed for peer s.
Fig.2. Initial states of peer t, w, x, y, z, and s, showing DTI and ITI tables
3.5Private History Verification
Once a path has been found using the shared history, the verification process
starts. This process queries every intermediate peer on that path P(s,x,...,t),
where s is the source, t is the target and x,... are intermediate peers, to is-
sue a check. An intermediate peer receives a request containing the source, the
predecessor and successor peers. The intermediate peer checks and accounts the
balance of the predecessor and successor peers, with the effect that the intermedi-
ate peer transfers its debts from the predecessor to the source. The intermediate
peer sends a signed check with the new balance to the source peer. Each inter-
mediate peer is requested sequentially to send a check to the requesting peer
until the successor peer is the target peer. If an intermediate peer fails to send
a check, e.g., because of an imbalance due to old history data, then the path is
In Figure 3, peer s requests a resource with value 2 from peer t (1), assuming
the initial state in Figure 2. Since the credit limit has been set to 1, peer t
reports (2) that this exceeds the credit limit. Peer s evaluates its DTI and ITI
tables. The ITI table of peer s contains the DTI from peer x (cf. Figure 2). The
negative response for the request contains the DTI of peer t, so peer s updates
its ITI accordingly in (2). Then, peer s searches a path using the ITI and DTI
tables using breadth-first search and finds s → x → w → t.
Fig.3. Example: Peer s requests resources form peer t
In Figure 3 peer s requests from peer x (3) a check. Peer x settles and updates
its DTIs for peer w and peer s to DTIx(s) = 0 and DTIx(w) = 0. The check
that is sent back (4) contains a signed message with STw(s) = 1, where ST
stands for settled transaction. Peer s contacts peer w (5) with this check and
asks peer w to settle and update its DTIs for peer t and peer x, which results in
DTIw(x) = 0 and DTIw(s) = 0, respectively. Then a check STt(s) = 1 is sent
back (6). Peer s requests resources from peer t (7) and provides the check from
peer w. Peer t settles and updates its DTI to DTIt(s) = 1 and DTIt(w) = 0.
The request for a resource with value 2 is granted (8). After this transaction,
peer t updates its DTI table with DTIt(s) = −1.
4Implementation and Simulation
PSH has been implemented and tested using Java 1.6. Message communication
between peers is asynchronous. For short messages UDP is used, otherwise TCP.
The time decay function fdecay(transaction) is implemented as a queue with
up to 100 entries to keep memory usage low. This means that the DTI and
ITI tables contain up to 100 transaction entries each. The oldest entry will be
removed if this limit is exceeded. An entry contains credit amount and node
address. The transferred subset of DTI values is limited to 6 as a compromise
between message size and data propagation.
Two versions of PSH have been simulated and compared to TFT: PSH as
described in Section 3, and PSH with a reduced number of message transfers
(PSH r), both with L = 3. The reduction has been achieved by sending a subset
of the DTI only if a request failed. PSH r does not retry to send the request
after a failed transaction, while PSH retries up to 3 times. A retry in PSH can
be successful if a path can be found and verified. A retry in TFT would always
fail because a peer only updates its history after a successful transaction. In
contrast, PSH may update its history with a check from another peer and a
retry may be successful.
All simulations have been performed with 100 peers that share resources. Every
peer has always exactly one resource, and requests 10 times a randomly chosen
resource. Each simulation is run 10 times; averaged results are displayed. The
simulation is repeated with the number of unique resources in the system varying
from 1 to 100. In a system with only one unique resource, every peer requests
the same resource, thus the interest is symmetric. With 100 unique resources on
100 nodes, there is a high probability that interest is asymmetric.
The success ratio is defined as s/(f + s), where s is the number of successful
transactions and f is the number of failed transactions. As shown in Figure 4, the
success ratio of PSH is always higher than TFT, particularly when the number
of unique resources is around 32. The higher success ratio is due to the shared
history data. PSH r is at an intermediate position, performing better than TFT,
but worse than PSH, since the algorithm does not retry transactions.
The message count represents the total number of messages per transaction
sent through the network. The total message size shows the total amount of bytes
sent over the network. The message count and size include resource request and
response messages, and for PSH additionally check request and reply messages.
In Figure 5, the message count for TFT is constant at 2 messages per trans-
action, since a transaction consists of a request and a reply message. In PSH,
besides resource requests, peers also exchange checks, therefore, the number of
Fig.4. Transaction success ratio for PSH, PSH(r), and TFT
messages is higher — up to twice as many as TFT, peaking on 32 unique re-
sources. An important factor that contributes to this high number is the PSH
retry behavior. Since PSH r does not perform retries, its number of messages
are, on average, only 2.22% above TFT, at maximum 6.4%.
In Figure 6, the message size is higher for PSH and PSH r than for TFT due
to two factors: (1) the message count is larger, and (2) messages contain also
history information. The message size of PSH r is smaller than PSH because of
fewer history information.
Both Figures 5 and 6 show a similar behavior for PSH. First message size
and count increases. This is due to the fact that the number of unique resources
decreases. This leads to a decrease of the success ratio, which leads to message
retries, with up to 3 retries. At the peak level, for about 33 unique resources
(32 in our particular simulation), one resource is kept by approximately 3 peers.
From that point on less than 3 peers have the same resource, thus, PSH sends
fewer retry messages and the message size and count decreases.
5 Summary, Discussion, Conclusion, and Future Work
This paper presents PSH, a transitive TFT incentive mechanism, which has a
higher transaction success ratio than TFT for peers with indirect reciprocity.
Fig.5. Message count of PSH, PSH(r), and TFT per transaction
Simulations show the trade-off between a higher success ratio and a higher mes-
sage size and count. PSH has a success ratio that is up to 73% higher than TFT.
The message size is up to 5.6 times higher and the message count up to 2 times
higher. However, with PSH modifications such as PSH r, the success ratio can
be increased, when compared to TFT, with only a small overhead with respect
to message size (49% larger than TFT in average) and count (2.2% larger than
TFT in average).
A subset of history information is propagated together with request and response
messages. Thus, if a peer does not interact with any other peer, history informa-
tion does not flow and already stored information becomes inaccurate. A path
that relies on inaccurate data will fail. In such cases, PSH retries the request,
which result in a higher message size and count. To avoid this problem, a proper
time decay function is applied.
A check from a peer has to be signed and thus, the public key has to be
transferred first. Besides the message overhead, signing a message is a CPU
intensive task. Thus, PSH is not suitable for micro-trading, i.e. trading many
small resources in short time.
Fig.6. Total message size of PSH, PSH(r), and TFT
PSH works better than TFT in systems with many different resources, i.e. in
systems with an asymmetry of interest. This paper shows that there is potential
to improve TFT by introducing PSH. The cost of a higher transaction success
ratio is an increased message size. If a high transaction rate is more important
than bandwidth constraints, then PSH should be preferred over TFT.
5.3 Future Work
The PSH mechanism could also be used for trust and reputation management.
As the collected history information is locally available, the trust value can be
calculated for each request and additional variables could be considered in the
The PSH mechanism has been thoroughly tested and simulated. While this
first step of the evaluation shows that the mechanism works as expected, for the
second step of the evaluation, further measurements in a distributed environment
such as PlanetLab with more than 100 nodes are needed, in order to show that
PSH is robust, fault and delay tolerant, and scalable. Future work will measure
how PSH works with collusion and false reports, while varying the number of
retries and the credit limit.
This work has been performed partially in the framework of the EU IST Project
EC-GIN (FP6-2006-IST-045256) as well as of the EU IST NoE EMANICS (FP6-
2004-IST-026854). The authors acknowledge valuable feedback from their col-
leagues and project partners.
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