Transcript Missie en Visie TU Delft

Reducing the History in Decentralized
Interaction-Based Reputation Systems
Dimitra Gkorou, Tamás Vinkó, Nitin Chiluka,
Johan Pouwelse, and Dick Epema
Parallel and Distributed Systems Group,
Delft University of Technology, the Netherlands
May 22, 2012
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Overview
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Interaction-based Reputation Systems
Limitations of the Complete History
Reducing the History
Evaluation
Conclusion
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Reputation Systems: Basic Concepts
• the goal of reputation in large scale systems:
• establish trust among users
• incentives for good behavior
• Interaction-Based Reputation Systems
complete
history of
interactions
reputation
algorithm
reputations of
nodes
• why not use the complete history?
• resource requirements: computation + storage capacity
• dynamic behavior: population turnover + changing behavior
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Reducing the History: Basic Approach
• Complete History (CH): modeled as a growing directed
weighted graph
• Reduced History (RH): a dynamically maintained subset
of CH with fixed size
• removal of the least important nodes and edges in
node removal:
freshness
activity level
reputation
position
edge removal:
freshness
weight
position
Complete History (CH)
Reduced History (RH)
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Reducing the History: Priority Score
• parameters indicating the importance:
• freshness (node/edge): capturing the dynamics of the system
• position (node/edge): keeping the graph connected
• activity level (node): maintaining informative nodes
• reputation (node): maintaining trustworthy information
• weight (edge): importance of an edge
• combined to a priority score for each node and edge
Complete History (CH)
Reduced History (RH)
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Reduced History: Construction
Complete History:
• add new node + its edges
• add new edge connecting existing nodes
Reduced History (fixed size):
• add the new node + remove the node with the lowest
priority score
• add the new edge + remove the edge with the lowest
priority score
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Complete History
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Reduced History
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Experiment Setup: Synthetic Graphs
• CH growing up to 5000 nodes
• random graphs:
• new nodes/edges connected to existent nodes with a constant
probability
• scale-free graphs:
• new nodes/edges connected to existent nodes with a
probability proportional to their degree
• multiple edges correspond to weights
random graph
scale-free graph
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Experiment Setup: Real-world Graphs
• Bartercast Reputation mechanism:
• Tribler: the BitTorrent P2P file-sharing system
• provides incentives for contribution
• peers locally store the history of their own interactions +
interactions among other peers
• information exchange: using an epidemic protocol
• Bartercast graph:
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crawled the Bartercast reputation mechanism (4 months)
union of all local graphs
vertices: the peers of Tribler
weighted edges: the amount of the transferred data between
two peers
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Experiment Setup: real-world graphs
• Author-to- author citation graph:
• derived from papers published in Physical Review E
• vertices: the authors of papers
• weighted edges: number of citations between authors
graph
# nodes
# edges
aver. path length
c.c.
Bartercast
10,634
31,624
2.64
0.00074
Citation
15,360
365,319
3.29
0.11
• small-world graphs
• Citation graph more densely connected than Bartercast
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Computation of Reputation
• Max-flow based computation:
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reputation computation of Bartercast
the weights of edges graph as flows
starting from the most central node j
reputation of peer a: the difference of flows faj and fja
• Eigenvector centrality:
• well-studied metric
• interactions with highly reputed nodes contribute more
• Pagerank
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Evaluation Metrics
• the ranking of reputations is more important than their
actual values
• the identification of the highest ranked nodes is more
important
• consider the sequences of ranked nodes in CH and RH
according to their reputation
• two metrics
• ranking error: the minimum number of swaps needed to
get the same ranking sequence in RH and CH
• ranking overlap: the fraction of common nodes in the
sequences of top-raked nodes in RH and CH
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Evaluation: ranking error
Pagerank
Max-Flow
Pagerank
ranking error
Max-Flow
Size of RH relatively to the size of CH
Growth of CH relatively to the size of RH
• scale-free and real-world graphs exhibit smaller
ranking error
• pagerank exhibits smaller ranking error
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Evaluation: ranking overlap
Ranking overlap
Max-Flow
• max-flow achieves much higher ranking overlap
Pagerank
Ranking overlap
• random graphs exhibit the worst ranking overlap
Size of RH relatively to the size of CH
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Evaluation: ranking overlap
Ranking overlap
Max-Flow
Ranking overlap
Pagerank
• max-flow achieves much higher again ranking overlap
Growth of CH relatively to the size of RH
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Conclusions
• the performance of RH depends on the topology
• scale-free and real-world graphs exhibit smaller ranking error and
higher ranking overlap
• the performance of RH depends on the reputation algorithm
• pagerank achieves lower ranking error
• max-flow achieves higher ranking overlap
• RH achieves good accuracy for real-world graphs
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Questions?
www.pds.ewi.tudelft.nl
www.tribler.org
contact: [email protected]
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