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

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  • GitHub repo 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.

  • GitHub repo 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.

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    Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.

  • GitHub repo 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.

  • GitHub repo 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.

  • GitHub repo cerebro

    Cerebro: A platform for Secure Coopetitive Learning

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

  • GitHub repo delphi

    A Cryptographic Inference Service for Neural Networks (by mc2-project)

    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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