Proof-of-Learning: Definitions and Practice

@article{Jia2021ProofofLearningDA,
  title={Proof-of-Learning: Definitions and Practice},
  author={Hengrui Jia and Mohammad Yaghini and Christopher A. Choquette-Choo and Natalie Dullerud and Anvith Thudi and Varun Chandrasekaran and Nicolas Papernot},
  journal={2021 IEEE Symposium on Security and Privacy (SP)},
  year={2021},
  pages={1039-1056}
}
Training machine learning (ML) models typically involves expensive iterative optimization. Once the model’s final parameters are released, there is currently no mechanism for the entity which trained the model to prove that these parameters were indeed the result of this optimization procedure. Such a mechanism would support security of ML applications in several ways. For instance, it would simplify ownership resolution when multiple parties contest ownership of a specific model. It would also… 

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