Nearest neighbors

This document introduces the nearest neighbors non-parametric classification models that Concrete ML provides with a scikit-learn interface through the KNeighborsClassifier class.

Concrete ML	scikit-learn
KNeighborsClassifier	KNeighborsClassifier

Ciphertext format compatibility

These models only support Concrete ciphertexts. See the ciphertexts format documentation for more details.

Example

from concrete.ml.sklearn import KNeighborsClassifier

concrete_classifier = KNeighborsClassifier(n_bits=2, n_neighbors=3)

Quantization parameters

The KNeighborsClassifier class quantizes the training data-set provided to .fit using the specified number of bits (n_bits). To comply with accumulator size constraints, you must keep this value low. The model's accuracy will depend significantly on a well-chosen n_bits value and the dimensionality of the data.

The predict method of the KNeighborsClassifier performs the following steps:

1. Quantize the test vectors on clear data

2. Compute the top-k class indices of the closest training set vector on encrypted data

3. Vote for the top-k class labels to find the class for each test vector, performed on clear data

Inference time considerations

The FHE inference latency of this model is heavily influenced by the n_bits and the dimensionality of the data. Additionally, the data-set size has a linear impact on the data complexity. The number of nearest neighbors (n_neighbors) also affects performance.

The KNN computation executes in FHE in $O(Nlog^2k)$ steps, where $N$ is the training data-set size and $k$ is n_neighbors. Each step requires several PBS operations, with their runtime affected by the factors listed above. These factors determine the precision needed to represent the distances between test vectors and training data-set vectors. The PBS input precision required by the circuit is related to the precision of the distance values.