Key Concepts

Concrete-ML is built on top of Concrete-Numpy, which enables Numpy programs to be converted into FHE circuits.

Lifecycle of a Concrete-ML model

I. Model Development

  1. 1.
    Training. A model is trained using plaintext, non-encrypted, training data.
  2. 2.
    Quantization. The model is converted into an integer equivalent using quantization. Concrete-ML performs this step either during training (Quantization-Aware Training) or after training (Post-Training Quantization), depending on model type. Quantization converts inputs, model weights and all intermediate values of the inference computation to integers. More information is available here.
  3. 3.
    Simulation using the Virtual Library. Testing FHE models on very large datasets can take a long time. Furthermore, not all models are compatible with FHE constraints out-of-the-box. Simulation using the Virtual Library allows you to execute a model that was quantized, to measure the accuracy it would have in FHE, but also to determine the modifications required to make it FHE compatible. Simulation is described in more details here.
  4. 4.
    Compilation. Once the model is quantized, simulation can confirm it has good accuracy in FHE. The model then needs to be compiled using Concrete's FHE compiler to produce an equivalent FHE circuit. This circuit is represented as an MLIR program consisting of low level cryptographic operations. You can read more about FHE compilation here, MLIR here and about the low-level Concrete library here.
  5. 5.
    Inference. The compiled model can then be executed on encrypted data, once the proper keys have been generated. The model can also be deployed to a server and used to run private inference on encrypted inputs.
You can see some examples of the model development workflow here.

II. Model deployment

  1. 1.
    Client/Server deployment. In a client/server setting, the model can be exported in a way that:
    • allows the client to generate keys, encrypt and decrypt.
    • provides a compiled model that can run on the server to perform inference on encrypted data
  2. 2.
    Key generation. The data owner (client) needs to generate a pair of private keys (to encrypt/decrypt their data and results) and a public evaluation key (for the model's FHE evaluation on the server).
You can see an example of the model deployment workflow here.

Cryptography concepts

Concrete-ML and Concrete-Numpy are tools that hide away the details of the underlying cryptography scheme, called TFHE. However, some cryptography concepts are still useful when using these two toolkits:
  1. 1.
    Encryption/Decryption. These operations transform plaintext, i.e. human-readable information, into ciphertext, i.e. data that contains a form of the original plaintext that is unreadable by a human or computer without the proper key to decrypt it. Encryption takes plaintext and an encryption key and produces ciphertext, while decryption is the inverse operation.
  2. 2.
    Encrypted inference. FHE allows a third party to execute (i.e. run inference or predict) a machine learning model on encrypted data (a ciphertext). The result of the inference is also encrypted and can only be read by the person who gets the decryption key.
  3. 3.
    Keys. A key is a series of bits used within an encryption algorithm for encrypting data so that the corresponding ciphertext appears random.
  4. 4.
    Key generation. Cryptographic keys need to be generated using random number generators. Their size may be large and key generation may take a long time. However, keys only need to be generated once for each model a client uses.
  5. 5.
    Guaranteed correctness of encrypted computations. To achieve security, TFHE, the underlying encryption scheme, adds random noise as ciphertexts. This can induce errors during processing of encrypted data, depending on noise parameters. By default, Concrete-ML uses parameters that ensure the correctness of the encrypted computation, so you do not need to take into account the noise parametrization. Therefore, results on encrypted data will be the same as the results of simulation on clear data.
While Concrete-ML users only need to understand the cryptography concepts above, for a deeper understanding of the cryptography behind the Concrete stack, please see the whitepaper on TFHE and Programmable Boostrapping or this series of blogs.

Model accuracy considerations under FHE constraints

To respect FHE constraints, all numerical programs over encrypted data must have all inputs, constants and intermediate values represented with integers of a maximum of 8 bits.
Thus, Concrete-ML quantizes the input data and model outputs in the same way as weights and activations. The main levers to control accumulator bit-width are the numbers of bits used for the inputs, weights and activations of the model. These parameters are crucial to comply with the constraint on accumulator bit-widths. Please refer to the quantization documentation for more details about how to develop models with quantization in Concrete-ML.
However, these methods may cause a reduction in the accuracy of the model since its representative power is diminished. Most importantly, carefully choosing a quantization approach can alleviate accuracy loss, all the while allowing compilation to FHE. Concrete-ML offers built-in models that already include quantization algorithms, and users only need to configure some of their parameters, such as the number of bits, discussed above. See the advanced quantization guide for information about configuring these parameters for various models.
Additional specific methods can help to make models compatible with FHE constraints. For instance, dimensionality reduction can reduce the number of input features and, thus, the maximum accumulator bit-width reached within a circuit. Similarly, sparsity-inducing training methods, such as pruning, de-activate some features during inference, which also helps. For now, dimensionality reduction is considered as a pre-processing step, while pruning is used in the built-in neural networks.
The configuration of model quantization parameters is illustrated in the advanced examples for Linear and Logistic Regressions and dimensionality reduction is shown in the Poisson regression example.