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strongest possible reputation system #31

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Idea: strongest possible reputation system by combining all existing science.

Problem: our scientists Rahim, Adele, Nitin and Dimitra did not combine their work.
Reason: optimal publication strategy is one clean and simple idea
There are no joint publications, no re-usage of algorithms and little collaboration.

We evaluated in isolation multiple enhancements to make accounting of contributions stronger and incentive compatible:

  • max-flow calculation
  • delete old information to reduce storage cost
  • depletion of often used paths, provides sybil resilience with additional bookkeeping
  • introduce negative trust, these negative edges increase sybil attack resilience
  • friendship as an edge property to strengthen the trust overlay against sybils
  • age as an peer property to strengthen against sybil attack
  • biased random walk, taking into account peer and edge properties
  • double signed signature (proof-of-work)
  • append-only signatures, misreporting attack degrades it to weaker white-washing attack
  • running total account of consumed and contributed resources, degrades misreporting attack
  • transfer encrypted information, only provide decode key after proof-of-work is signed
  • incremental signatures, reduce risk-taking by demanding signatures during transfer
  • use n-hop Max-Path to reduce compulational cost
  • reason from betweenness centrality node
  • use full gossip

Reputation system proposals often assume either:

  1. Tamper-proof and scalable storage of information
  2. Scalable storage of information

Option number 1 is beyond the state-of-the-art, without assuming an honest central server with infinite capacity. Option number 2 is difficult to achieve. For scalability seasons, the storage layer will always be imperfect and will never offer both instant, accurate and complete recall of information. Thus we need to be able to tolerate limited accuracy or coverage in addition to the usual misreporting, lying and sybil creation.

A reputations system needs to determine (assuming background gossip of trust vectors):
 1 Who to talk to
 2 how to reach them
 3 What to say
 4 How much to trust

Existing work is focused on no.4 mostly, while the others have proven to be
much harder to solve. Nobody identified them as a problems and no
solutions exist.
Trust calculation is just 50 lines of code, while no. 1-3 is beyond
the state-of-the-art in epidemic protocols.

Missing topics:
3. "what to say", Efficient synchronisation of BarterCast (subgraph) trust vectors

Existing material:
 1. Who to talk to
    Biased random walk
 2. how to reach them
    Churn resiliance, state preservation, live overlay, NAT traversal, etc
 4. How much to trust
   Meulpolder 2008: Max-flow
   Rahim: various sybil-limiters
   Adele: rewards also effort
   Dimitra: betweenness centrality
   Nitin: sybil-limiters

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