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Implementation of a robust and scalable consensus protocol for blockchain Raphal Dunant DEDIS lab Supervisors: Linus Gasser, Eleftherios Kokoris-Kogias Responsible: Prof. Bryan Ford Proposal cosigning Time or timestamp services


  1. Implementation of a robust and scalable consensus protocol for blockchain Raphaël Dunant DEDIS lab Supervisors: Linus Gasser, Eleftherios Kokoris-Kogias Responsible: Prof. Bryan Ford

  2. Proposal cosigning ● Time or timestamp services ● Certificate Authorities (CAs) ● Directory authorities ● Software update services ● Digital notaries ● Randomness services 2

  3. Summary ● Introduction (done) ● CoSi protocol ● Work done (challenges and found solutions) ● Simulation results ● Conclusion (results, lessons learned, etc.) 3

  4. CoSi: Decentralized Witness Cosigning 4

  5. CoSi: Decentralized Witness Cosigning 6

  6. Objectives ● Have a solid implementation of the CoSi protocol ● Compatible with ONet and Kyber libraries ● Handle failing nodes ● Clean, tested and documented code 7

  7. Tree generation 8

  8. Failing nodes 9

  9. Multiple sub-protocols 10

  10. Unit tests and documentation 11

  11. Simulation results : complete working tree ● 50ms delay, 10Mb/s bandwidth ● 4 machines, 4x24 threads, 2.5 GHz, 4x30MB cache, 4x256GB DDR4-2133 RAM 12

  12. Simulation results : failing subleaders ● 500 nodes, ⌊ √500 =22 subleaders ⌋ ● 50ms delay, 10Mb/s bandwidth ● 4 machines, 4x24 threads, 2.5 GHz, 4x30MB cache, 4x256GB DDR4-2133 RAM 13

  13. Simulation results : failing leafs ● 500 nodes, ⌊ √500 =22 subleaders ⌋ ● 50ms delay, 10Mb/s bandwidth ● 4 machines, 4x24 threads, 2.5 GHz, 4x30MB cache, 4x256GB DDR4-2133 RAM 14

  14. Future work ● BFT-CoSi ● Handle root-node failure ● Handle finely nodes failures during runtime ● Extend unit tests ● Implement on a real blockchain ● Use ONet v2 ● Use Omniledger’s Sharding Via Bias-Resistant Distributed Randomness 15

  15. Conclusion ● Complete and working CoSi implementation with node failure ● Easy to use, with documentation and examples ● Lots of interest ● Scalable and tested ● Can still get better ● Personal improvement 16

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