1 NETWORKING 2012 Parallel and Distributed Systems Group, Delft University of Technology, the Netherlands May 22, 2012 Reducing the History in Decentralized Interaction-Based Reputation Systems Dimitra Gkorou, Tamás Vinkó, Nitin Chiluka, Johan Pouwelse, and Dick Epema

2 NETWORKING 2012 Overview Interaction-based Reputation Systems Limitations of the Complete History Reducing the History Evaluation Conclusion

3 NETWORKING 2012 Reputation Systems: Basic Concepts the goal of reputation in large scale systems: establish trust among users incentives for good behavior Interaction-Based Reputation Systems why not use the complete history? resource requirements: computation + storage capacity dynamic behavior: population turnover + changing behavior reputation algorithm complete history of interactions reputations of nodes

4 NETWORKING 2012 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 Reducing the History: Basic Approach Complete History (CH) Reduced History (RH) node removal: freshness activity level reputation position edge removal: freshness weight position

5 NETWORKING 2012 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 Reducing the History: Priority Score Complete History (CH) Reduced History (RH)

6 NETWORKING 2012 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 w ad w ab w bc wdwd Complete History w ad w ab w bc wdwd Reduced History w ed w ce wgwg b a d b a d c c e e f f g g w fg w ge w fe

7 NETWORKING 2012 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

8 NETWORKING 2012 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 i nformation exchange: using an epidemic protocol Bartercast graph: 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

9 NETWORKING 2012 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 small-world graphs Citation graph more densely connected than Bartercast graph# nodes# edgesaver. path lengthc.c. Bartercast10,63431, Citation15,360365,

10 NETWORKING 2012 Computation of Reputation Max-flow based computation: 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 f aj and f ja Eigenvector centrality: well-studied metric interactions with highly reputed nodes contribute more Pagerank e j c b W ca\ac w bi f w gk k w bj g w jg w fg w ge W ba\ab w ga a w ia i

11 NETWORKING 2012 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

12 NETWORKING 2012 Evaluation: ranking error Max-Flow Pagerank ranking error Size of RH relatively to the size of CH Growth of CH relatively to the size of RH Max-Flow Pagerank scale-free and real-world graphs exhibit smaller ranking error pagerank exhibits smaller ranking error

13 NETWORKING 2012 Evaluation: ranking overlap Max-Flow Ranking overlap Size of RH relatively to the size of CH Pagerank max-flow achieves much higher ranking overlap random graphs exhibit the worst ranking overlap

14 NETWORKING 2012 Evaluation: ranking overlap Max-Flow Ranking overlap Pagerank Growth of CH relatively to the size of RH max-flow achieves much higher again ranking overlap

15 NETWORKING 2012 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

16 NETWORKING 2012 Questions? contact: