Announcing MC²: Securely perform analytics and machine learning on confidential data

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • mc2

    A Platform for Secure Analytics and Machine Learning

    We are excited to announce the initial release of the open source MC2 Project, a collection of tools for computing and collaborating on confidential data. Developed by our team in the UC Berkeley RISELab, MC2 (Multi-Party Collaboration and Coopetition) enables rich analytics and machine learning on encrypted data, ensuring that data remains concealed even when it’s being processed. The data in use remains hidden from the server running the job, allowing confidential workloads to be offloaded to untrusted third parties or cloud providers. This not only protects confidential data from intrusions, but also enables secure collaboration -- multiple data owners can jointly run analytics or ML on their collective data, without explicitly revealing their individual data to anyone else: not even a trusted third party.

  • opaque-sql

    An encrypted data analytics platform

    The MC2 Compute Services: MC2 offers several compute services: these include Spark SQL, distributed XGBoost, and secure aggregation for federated learning. All are intended to run in a primarily untrusted environment, such as a cluster of machines hosted on a public cloud, that has support for trusted execution environments (hardware enclaves). Data is encrypted in transit using a client key and only ever decrypted inside hardware enclaves, providing the previously mentioned security guarantees for data-in-use. For all compute services, MC2 leverages the Open Enclave SDK, a project intended to provide a consistent API for a variety of different enclave architectures.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  • secure-xgboost

    Secure collaborative training and inference for XGBoost.

    The MC2 Compute Services: MC2 offers several compute services: these include Spark SQL, distributed XGBoost, and secure aggregation for federated learning. All are intended to run in a primarily untrusted environment, such as a cluster of machines hosted on a public cloud, that has support for trusted execution environments (hardware enclaves). Data is encrypted in transit using a client key and only ever decrypted inside hardware enclaves, providing the previously mentioned security guarantees for data-in-use. For all compute services, MC2 leverages the Open Enclave SDK, a project intended to provide a consistent API for a variety of different enclave architectures.

  • secure-aggregation

    Secure aggregation for federated learning using enclaves

    The MC2 Compute Services: MC2 offers several compute services: these include Spark SQL, distributed XGBoost, and secure aggregation for federated learning. All are intended to run in a primarily untrusted environment, such as a cluster of machines hosted on a public cloud, that has support for trusted execution environments (hardware enclaves). Data is encrypted in transit using a client key and only ever decrypted inside hardware enclaves, providing the previously mentioned security guarantees for data-in-use. For all compute services, MC2 leverages the Open Enclave SDK, a project intended to provide a consistent API for a variety of different enclave architectures.

  • cerebro

    Cerebro: A platform for Secure Coopetitive Learning (by mc2-project)

    Cerebro: A general purpose Python DSL for learning with secure multiparty computation.

  • delphi

    A Cryptographic Inference Service for Neural Networks

    Delphi: Secure inference for deep neural networks.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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