Reducing the History in Decentralized Interaction-Based Reputation Systems

1. Reducing the History in Decentralized Interaction-Based Reputation Systems Dimitra Gkorou, TamásVinkó, Nitin Chiluka, Johan Pouwelse, and Dick Epema

2. Overview • Interaction-based Reputation Systems • Limitations of the Complete History • Reducing the History • Evaluation • Conclusion

3. 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 complete history of interactions reputation algorithm reputations of nodes

4. 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 fixedsize • 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)

5. 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):maintainingtrustworthy information • weight (edge):importance of an edge • combined to a priorityscore for each node and edge Complete History (CH) Reduced History (RH)

6. 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 a a wad wad d d wab wab c c wce wg wed wed wbc wbc b b wd e wd e wfe wge wfe wge f f wfg g wfg g Complete History Reduced History

7. 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 connectedto existent nodes with a probability proportional to their degree • multiple edges correspond to weights random graph scale-free graph

8. 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: • crawled the Bartercastreputation 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. 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

10. Computation of Reputation • Max-flow based computation: • reputation computation of Bartercast • the weights of edges graphas flows • starting from the most central node j • reputation of peer a: the differenceof flows faj and fja • Eigenvector centrality: • well-studied metric • interactions with highly reputed nodes contribute more • Pagerank a wia Wca\ac c f wga wfg Wba\ab i wbi g wgk j b wbj wge wjg k e

11. 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. Evaluation: ranking error Pagerank Max-Flow Max-Flow Pagerank ranking error 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

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

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

15. 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. Questions? www.pds.ewi.tudelft.nl www.tribler.org contact: d.gkorou@tudelft.nl