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Curated Proof Markets & Token-Curated Identities in Ocean Protocol

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This talk describes Ocean Protocol’s token mechanics via step-by-step examples of how users earn tokens by curating data and making it available.

Blog post: https://medium.com/@trentmc0/curated-proofs-markets-a-walk-through-of-oceans-core-token-mechanics-3d50851a8005

Presented at 9984 Blockchain Meetup, Berlin, Mar 28, 2018

Published in: Technology
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Curated Proof Markets & Token-Curated Identities in Ocean Protocol

  1. 1. Curated Proof Markets & Token-Curated Identities in Ocean Protocol Trent McConaghy @trentmc0
  2. 2. #Data #Incentives
  3. 3. Audio radar
  4. 4. 1000x more data The Unreasonable Effectiveness of Data 1000% less error!
  5. 5. Silo mo’ data Mo’ accuracy Mo’ $ Default incentive: hoard the data
  6. 6. “Show me the incentive and I will show you the outcome.” -Charlie Munger
  7. 7. You can get people to do stuff by rewarding them with tokens. This is a superpower.
  8. 8. Change the incentives! Silo Pool mo’ data Mo’ accuracy Mo’ $
  9. 9. How to design? Here’s a Process for Token Engineering 1. Formulate the problem. Objectives, constraints, design space. 2. Try an existing pattern. If needed, try different formulations or solvers. 3. Design new pattern?
  10. 10. Patterns (Building Blocks) for Token Design • Curation • Stake machines, curation markets • Curated proofs markets, TCRs • Proofs • Bitcoin puzzle solving, Filecoin PoST, Steemit human • Data availability, more • Identity • Reputation • Governance / software updates • Third-party arbitration • …
  11. 11. Patterns (Building Blocks) for Token Design • Curation • Stake machines, curation markets • This talk: Curated proofs markets, TCRs • Proofs • Bitcoin puzzle solving, Filecoin PoST, Steemit human • This talk: Data availability, more • Identity • Reputation • Governance / software updates • Third-party arbitration • …
  12. 12. Curated Proofs Markets: Formulation
  13. 13. Objective: maximize supply of relevant data • We convert this objective to block rewards. • Then, how about this reward: “Reward making relevant data available when asked” • But: How to know what’s relevant? Algorithms capture this poorly. • Solution: leave it to the crowd. Let them put their money where their mouth is! (Staking) • Revised reward: “Reward curating data (staking on it) + making it available”
  14. 14. Objective: maximize supply of relevant data • Reward curating data (staking on it) + making it available • New pattern: curated proofs market E(Rij) α log10(Sij) * log10(Dj) * T *Ri Expected reward for user i on dataset j Dj = proofed popularity = # times made dataset available Sij = predicted popularity = user’s curation market stake in dataset j # tokens during interval
  15. 15. From AI data to AI services Motivations: • Privacy, so compute on-premise or decentralized • Data is heavy, so compute on-premise • Link in emerging decentralized AI compute Objective function: Maximize supply of relevant services =reward curating services + proving that it was delivered E(Rij) α log10(Sij) * log10(Dj) * T *Ri proofed popularity of service predicted popularity of service
  16. 16. E(Rij) α log10(Sij) * log10(Dj) * T *Ri Sij = expected popularity = stake in dataset j Key block #1: Curation market Dj = actual popularity = # downloads Key block #2: Proof of availability (All other blocks are ancillary to these blocks)
  17. 17. Proofs
  18. 18. E(Rij) α log10(Sij) * log10(Dj) * T *Ri Sij = predicted popularity = stake in dataset j Key block #1: Curation market Dj = actual popularity = # downloads Key block #2: Proof of availability (All other blocks are ancillary to these blocks)
  19. 19. Proof of Data Availability Approach A: FileCoin Proof of Space-Time (PoST), though that’s overkill Approach B: TrueBit-style challenge-response: 1. I serve data to you 2. If you don’t think you got it, you challenge (with stake) 1. Randomly choose two actors to vet 2. Actors vote on whether served 3. If they agree that I served it, you lose stake 4. If they agree that I didn’t serve it , I lose stake 5. Else
  20. 20. More Proofs
  21. 21. With more proofs, more services! Blob data Availability AI compute Privacy compute Generalized Proofs Inter- Operability *Note: logos shown are examples and do not imply partnerships or integrations General compute, prediction Structured data Availability
  22. 22. Token Curated Registry (TCR)
  23. 23. First, some attack vectors
  24. 24. Some attack vectors • Elsa & Anna Attack: Someone uploads popular content that they don’t have rights to • Data Escapes: People take data out of the system • Curation Clones: Others create a new market for an existing dataset • Sybil Downloads: Someone (or a ring of buddies) downloads a dataset they own >>1 times, to get >>rewards
  25. 25. How to approach? Idea 1: network directly has legal arbitration • Problem: it binds the network to jurisdictions (eek). • Need to be permissionless and borderless!! Idea 2: “sunny day tech, rainy day law” • Core network itself has self-regulation by community. Incentivize good acting. Zero sum  positive sum. 99% case. • Higher levels can have arbitration tied to jurisdictions. 1% case
  26. 26. class TokenCuratedRegistry def propose(data) def challenge(proposal) def vote(challenge) TCR: a whitelist of good actors, curated by the actors themselves New user B proposes for B to join 10 tokens stake by B Existing user A challenges User status A (Alice) “Challenger” B (Bob) “New” + “Proposed” Mallory “OK” Trent “OK” 10 tokens stake by A
  27. 27. class TokenCuratedRegistry def propose(data) def challenge(proposal) def vote(challenge) TCR: a whitelist of good actors, curated by the actors themselves New user B proposes for B to join 10 tokens stake by B Existing user A challenges User status A (Alice) “Challenger” B (Bob) “New” + “Proposed” Mallory “OK” Trent “OK” 10 tokens stake by A Concern: High friction onboarding 7-28 day waiting period
  28. 28. class TokenCuratedRegistry def vouch(data) def challenge(proposal) def vote(challenge) TCR: a whitelist of good actors “Trust is risk” for low-friction onboarding: vouch for others Existing user A vouches for new user B to join 10 tokens stake by A, B can run away with it anytime User status A (Alice) “Vouches” B (Bob) “New” Mallory “OK” Trent “OK”
  29. 29. Some attack vectors • Data Escapes: People take data out of the system • Curation Clones: Others create a new market for an existing dataset • Elsa & Anna Attack: Someone uploads popular content that they don’t have rights to • Sybil Downloads: Someone (or a ring of buddies) downloads a dataset they own >>1 times, to get >>rewards
  30. 30. Walk Through
  31. 31. Conclusion
  32. 32. AI  Data silos  data crisis. Trent McConaghy @trentmc0 A thoughtful token design has thoughtful building blocks. For Ocean, this includes: • Curated Proofs Markets (CPMs) • Token Curated Registry

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