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@ sara
2023-06-02 03:17:17Machine learning (ML) is a subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. ML models typically have three primary components:
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Training Data: A set of input data that is used to train a machine learning algorithm to make predictions or classify new data. Training data can take many forms, such as images, text, audio, numerical data, or a combination of these.
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Model Architecture: The overall structure or design of a machine learning model. It defines the types and number of layers, activation functions, and connections between nodes or neurons. The choice of architecture depends on the specific problem and data being used.
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Model Parameters: Values or weights that the model learns during the training process to make predictions. These values are adjusted iteratively through an optimization algorithm to minimize the error between predicted and actual outcomes.
Models are produced and deployed in two phases:
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Training Phase: During training, the model is exposed to a labeled dataset and adjusts its parameters to minimize the error between predicted and actual outcomes. The training process typically involves several iterations or epochs, and the accuracy of the model is evaluated on a separate validation set.
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Inference Phase: The inference phase is when a trained machine learning model is used to make predictions on new, unseen data. The model takes in input data and applies the learned parameters to generate an output, such as a classification or regression prediction.
zkML today is primarily focused on the inference stage of ML models rather than on the training phase primarily due to the computational complexity of verifying training in-circuit. zkML’s focus on verifying inference is not a limitation though: we expect some very interesting use cases and applications to be produced from the inference phase.
Verified Inference Scenarios
There are four possible scenarios for verified inference:
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Private Input, Public Model. A Model Consumer (MC) may wish to keep their inputs private from a Model Provider (MP). For example, a MC may wish to prove the result of a credit-scoring model to a lender without disclosing their personal financial information. This could be done using a pre-commitment scheme and running the model locally.
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Public Input, Private Model. A common issue with ML-as-a-Service is that the MP may wish to hide their parameters or weights to protect their IP, and the MC wants to verify that the generated inferences do in fact come from the specified model in an adversarial setting. Think of it this way: a MP has an incentive to run a lighter model to save on costs when serving inferences to a MC. Using a commitment of the model weights on-chain, a MC can audit a private model at any time.
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Private Input, Private Model. This scenario arises when the data being used for inference is highly sensitive or confidential, and the model itself is hidden to protect IP. An example of this might include auditing a healthcare model using private patient information. Compositional techniques in zk or use of multi-party computation (MPC) or variations of FHE can be used to service this scenario.
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Public Input, Public Model. When all aspects of the model can be public, zkML solves for a different use case: compression and verification of off-chain computation to an on-chain environment. For larger models, it is more cost-effective to verify a succinct zk proof of an inference than to re-run the model themselves.
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