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https://github.com/IBM/ai-privacy-toolkit.git
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dbb958f791
Add AI privacy Dataset assessment module with two attack implementations. Signed-off-by: Maya Anderson <mayaa@il.ibm.com>
70 lines
3 KiB
Python
70 lines
3 KiB
Python
import abc
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import numpy as np
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from sklearn.neighbors import NearestNeighbors
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from tqdm import tqdm
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from apt.utils.datasets import ArrayDataset
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class AttackStrategyUtils(abc.ABC):
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"""
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Abstract base class for common utilities of various privacy attack strategies.
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"""
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pass
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class KNNAttackStrategyUtils(AttackStrategyUtils):
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"""
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Common utilities for attack strategy based on KNN distances.
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"""
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def __init__(self, use_batches: bool = False, batch_size: int = 10) -> None:
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"""
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:param use_batches: Use batches with a progress meter or not when finding KNNs for query set
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:param batch_size: if use_batches=True, the size of batch_size should be > 0
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"""
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self.use_batches = use_batches
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self.batch_size = batch_size
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if use_batches:
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if batch_size < 1:
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raise ValueError(f"When using batching batch_size should be > 0, and not {batch_size}")
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def fit(self, knn_learner: NearestNeighbors, dataset: ArrayDataset):
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knn_learner.fit(dataset.get_samples())
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def find_knn(self, knn_learner: NearestNeighbors, query_samples: ArrayDataset, distance_processor=None):
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"""
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Nearest neighbor search function.
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:param query_samples: query samples, to which nearest neighbors are to be found
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:param knn_learner: unsupervised learner for implementing neighbor searches, after it was fitted
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:param distance_processor: function for processing the distance into another more relevant metric per sample.
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Its input is an array representing distances (the distances returned by NearestNeighbors.kneighbors() ), and
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the output should be another array with distance-based values that enable to compute the final risk score
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:return:
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distances of the query samples to their nearest neighbors, or a metric based on that distance and calculated
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by the distance_processor function
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"""
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samples = query_samples.get_samples()
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if not self.use_batches:
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distances, _ = knn_learner.kneighbors(samples, return_distance=True)
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if distance_processor:
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return distance_processor(distances)
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else:
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return distances
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distances = []
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for i in tqdm(range(len(samples) // self.batch_size)):
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x_batch = samples[i * self.batch_size:(i + 1) * self.batch_size]
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x_batch = np.reshape(x_batch, [self.batch_size, -1])
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# dist_batch: distance between every query sample in batch to its KNNs among training samples
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dist_batch, _ = knn_learner.kneighbors(x_batch, return_distance=True)
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# The probability of each sample to be generated
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if distance_processor:
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distance_based_metric_per_sample_batch = distance_processor(dist_batch)
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distances.append(distance_based_metric_per_sample_batch)
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else:
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distances.append(dist_batch)
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return np.concatenate(distances)
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