secure-xgboost VS mc2

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.

Analytics and ML on confidential data are some interesting server side use cases. See the MC2 open source project, for example: https://github.com/mc2-project/mc2

Check out more blog posts on how to securely process data with MC² Project. We would love your contributions ✋ and support ⭐! Please check out the Github repo to see how you can contribute. No contribution is too small.

MC2 is a platform for running secure analytics on data that stays encrypted even when in use. By doing so, the project also enables secure collaboration among multiple organizations, where individual data owners can use our platform to jointly analyze their collective data without revealing it to one another. To learn more and to see the individual projects’ documentation, visit our landing page.

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.