Berkeley RDI and Polyhedra are excited to announce that they have developed a production-ready compiler that lets any AI developers utilize zkMLwithout any expertise in zero-knowledge proofs.
What is zkML?
zkML, a subset of verifiable computation, addresses trust in the context of AI by enabling proof of the correctness of model inference. Through zero-knowledge proofs, a service provider can demonstrate that a specific output was genuinely produced by running a given model on an input. Whether it’s a decision tree, neural network, or another machine learning model, zkML ensures verifiability without exposing underlying data or models.
Extended Capabilities of zkML
The potential of zkML extends beyond simple inference verification. It can be applied to:
- Data Origin Verification: Tracing and proving the lineage and authenticity of training data.
- Authenticated Data Labeling: Ensuring that data labeling processes are genuine and unaltered.
Find out more at https://www.polyhedra.network and https://rdi.berkeley.edu.
Berkeley RDI & Polyhedra Unveil Production-Ready zkML
Posted in Commentary with tags Berkeley RDI on November 19, 2024 by itnerdBerkeley RDI and Polyhedra are excited to announce that they have developed a production-ready compiler that lets any AI developers utilize zkMLwithout any expertise in zero-knowledge proofs.
What is zkML?
zkML, a subset of verifiable computation, addresses trust in the context of AI by enabling proof of the correctness of model inference. Through zero-knowledge proofs, a service provider can demonstrate that a specific output was genuinely produced by running a given model on an input. Whether it’s a decision tree, neural network, or another machine learning model, zkML ensures verifiability without exposing underlying data or models.
Extended Capabilities of zkML
The potential of zkML extends beyond simple inference verification. It can be applied to:
Find out more at https://www.polyhedra.network and https://rdi.berkeley.edu.
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