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Award ID contains: 2217058

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  1. ; ; ; (, Society for Industrial and Applied Mathematics)
    Free, publicly-accessible full text available January 1, 2026
  2. ; ; ; (, Society for Industrial and Applied Mathematics)
    SIAM (Ed.)
    Free, publicly-accessible full text available January 1, 2026
  3. ; ; ; (, NeurIPS Proceedings)
    Globerson, A; Mackey, L; Belgrave, D; Fan, A; Paquet, U; Tomczak, J; Zhang, C (Ed.)
    Free, publicly-accessible full text available December 8, 2025
  4. ; ; ; (, Proceedings of Machine Learning Research)
    Machine learning models are increasingly used in societal applications, yet legal and privacy concerns demand that they very often be kept confidential. Consequently, there is a growing distrust about the fairness properties of these models in the minds of consumers, who are often at the receiving end of model predictions. To this end, we propose FairProof– a system that uses Zero-Knowledge Proofs (a cryptographic primitive) to publicly verify the fairness of a model, while maintaining confidentiality. We also propose a fairness certification algorithm for fully-connected neural networks which is befitting to ZKPs and is used in this system. We implement FairProof in Gnark and demonstrate empirically that our system is practically feasible. 
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    Accepted Manuscript Full Text Available
  5. ; (, PMLR)
    Accepted Manuscript Full Text Available
  6. ; (, PMLR)
    Accepted Manuscript Full Text Available
  7. ; (, ICML/JMLR)
    Accepted Manuscript Full Text Available
  8. ; (, ICML)
    Accepted Manuscript Full Text Available
  9. ; ; (, IEEE International Symposium on Information Theory)
    Accepted Manuscript Full Text Available
  10. ; (, IEEE)
    Accepted Manuscript Full Text Available