Secure collaborative analytics and ML using MC²

Secure collaborative analytics and ML using MC²

We are excited to announce the initial release of the MC² Project, a collection of open source tools for computing and collaborating on confidential data. Developed at UC Berkeley’s 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. Read more

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