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Algorithmic mechanisms for reliable crowdsourcing computation under collusion.

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Summary

This study models worker processor collusion in computing systems using game theory. Simulations reveal a pure equilibrium benefits both master and workers, ensuring correct results even with collusion.

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Area of Science:

  • Distributed computing
  • Game theory
  • Computational economics

Background:

  • Master-worker computing systems face challenges with unreliable or malicious worker processors.
  • Collusion among worker processors can compromise task integrity and system efficiency.
  • Game theory provides a framework to analyze rational decision-making in strategic interactions.

Purpose of the Study:

  • To model worker processor collusion as a game-theoretic problem.
  • To identify conditions for a Nash Equilibrium ensuring correct task execution.
  • To evaluate the reliability-profit trade-offs of different equilibria in practice.

Main Methods:

  • Analytical identification of parameter conditions for a unique Nash Equilibrium.
  • Experimental evaluation of mixed equilibria.
  • Simulations to assess system performance under various collusive behaviors.

Main Results:

  • Analytical conditions for a unique Nash Equilibrium yielding correct results were identified.
  • Simulations demonstrated that a pure equilibrium, where no worker cheats, is often optimal.
  • This pure equilibrium provides benefits for both the master and worker processors, even under collusion.

Conclusions:

  • A pure equilibrium is robust and beneficial in master-worker systems with potential collusion.
  • Rational workers and masters achieve better outcomes by ensuring honest computation.
  • Game-theoretic modeling offers practical insights into securing distributed computing systems.