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@mpc-for-ppml

Multiparty Computation for Privacy-Preserving ML

Working on design and development of an end-to-end efficient MPC protocol for Privacy-Preserving Machine Learning (PPML)

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  1. mpyc mpyc Public template

    Forked from lschoe/mpyc

    🛠️ | A strong framework for Multiparty Computation (MPC).

    Python

  2. mpc-trial mpc-trial Public

    🧪 | Experimental repository for testing use case scenarios.

    Python

  3. core core Public

    👨‍💻 | Main repository of end-to-end MPC for PPML framework development.

    Python

  4. multiparty-psi multiparty-psi Public

    🌓 | Multi-party Private Set Intersection (PSI) protocols to handle data variation across participants.

    Python

  5. server server Public

    🗄️ | Server side implementation of end-to-end MPC protocol for PPML.

    Python

  6. client client Public

    💻 | Client side implementation of end-to-end MPC protocol for PPML.

    TypeScript

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