anomed-anonymizer 0.0.22

A library aiding to create anonymizers (privacy preserving machine learning models) for the AnoMed competition platform.

Anonymizer

A library aiding to create anonymizers (privacy preserving machine learning models) for the AnoMed competition platform.

Preliminaries

The AnoMed platform is basically a network of web servers which use web APIs to exchange data among each other and provide functionality to each other. Challenge web servers provide training and evaluation data, which may be requested via HTTP. They do also offer means to evaluate the utility of anonymizers (privacy preserving machine learning models) via HTTP and means to estimate the privacy of anonymizers via attacks on them (which we refer to by "deanonymizers" below). Anonymizer web servers offer input/output access, such that they may be attacked by deanonymizers. For more details about challenges or deanonymizers, view their corresponding repositories.

In general, you are free to create your own kind of anonymizer web server, as long as it offers some well described APIs and follows some general principles, which we will describe below. You do not need to use this library to submit anonymizers. However, if you would like to focus on defining the anonymizer itself, without being annoyed by web server related questions, use this library to generate web servers "for free", which integrate well with the AnoMed platform. If you plan to contribute to a challenge that uses one of our challenge web server templates, we very strongly suggest to make use of the accompanying anonymizer web server template provided in this repository, as all web-related issues have been taken care of by us already.

How to Create Anonymizer Web Servers (for selected use cases)

If you goal is to create an anonymizer that fits one of the following selected cases, you may use this library's template to create an anonymizer web server with minimal effort.

In the following we give some examples of how to use this library to create anonymizer submissions for the AnoMed platform. First, we cover some common cases for which we have created templates and then we tell you what to do, if these template do not suite your use case.

Anonymizer for Supervised Learning Challenges with Membership Inference Attack Threat Model

In this scenario we assume that the challenge you would like to contribute to has been created by the supervised_learning_MIA_challenge_server_factory function. Also, we assume that the challenge serves the Iris dataset, just like in the usage example of the AnoMed Challenge library. Now we know which routes are present, how to interact with them and what data to expect. The function supervised_learning_anonymizer_server_factory will create a suitable Falcon-based anonymizer web server for us. In the following example, we show how to use it:

import os

import anomed_anonymizer as anon
import numpy as np
from diffprivlib.models import GaussianNB

lower_bounds = 4 * [0.0]
upper_bounds = [10.0, 5.0, 10.0, 5.0]
estimator = GaussianNB(
    bounds=(lower_bounds, upper_bounds),
    priors=3 * [1.0 / 3.0],
)


def validate_feature_array(feature_array: np.ndarray) -> None:
    if feature_array.shape[1] != 4 or len(feature_array.shape) != 2:
        raise ValueError("Feature array needs to have shape (n_samples, 4).")
    if feature_array.dtype != np.float_:
        raise ValueError("Feature array must be an array of floats.")


example_anon = anon.WrappedAnonymizer(
    anonymizer=estimator,
    serializer=anon.pickle_anonymizer,
    feature_array_validator=validate_feature_array,
)

hostname = os.getenv("CHALLENGE_HOST")

# This is what GUnicorn expects
application = anon.supervised_learning_anonymizer_server_factory(
    anonymizer_identifier="example_anonymizer",
    anonymizer_obj=example_anon,
    model_filepath="anonymizer.pkl",
    default_batch_size=64,
    training_data_url=f"http://{hostname}/data/anonymizer/training",
    tuning_data_url=f"http://{hostname}/data/anonymizer/tuning",
    validation_data_url=f"http://{hostname}/data/anonymizer/training",
    utility_evaluation_url=f"http://{hostname}/utility/anonymizer",
    model_loader=anon.unpickle_anonymizer,
)

First, we create an allegedly privacy-preserving machine learning model – in this case, for example, an instance of GaussianNB. That model is a differentially private Gaussian naive Bayes classifier, which aims to solve a 3-class classification problem. Next, we define a feature array validation function that double-checks, whether the data received from the challenge is of the expected shape and type. See the docs of WrappedAnonymizer for more details. After that, we wrap the model, so that we receive an object of type SupervisedLearningAnonymizer, which is a necessary argument of supervised_learning_anonymizer_server_factory. Then we obtain the hostname of the challenge we would like to contribute to from an environment variable, which is set to the correct value by the AnoMed platform. Finally, we create the web application using supervised_learning_anonymizer_server_factory (for the meaning of the other arguments, see here). Use application as a target for, e.g., GUnicorn to serve the anonymizer web application.

The web app offers these routes (some may have query parameters not mentioned here):

• [GET] /: This displays an "alive message".
• [POST] /fit: This invokes fitting the Gaussian naive based classifier; the web app will then pull the training data from training_data_url.
• [POST] /evaluate: This invokes an intermediate, or final evaluation of the classifier, depending on query parameters.
• [POST] /predict: This offers a way to use the fitted anonymizer to predict the target values for any compatible feature array.

Anonymizer for Dataset Anonymization Challenges with ??? Threat Model

TODO

Anonymizer for Dataset Synthesis Challenges with ??? Threat Model

TODO

How To Create Challenge Web Servers Without Template

In case your goal is to contribute to a challenge, for which we do not offer a suitable anonymizer template, we suggest that you stick to the Falcon web framework and make use of at least some of the available resource building blocks. Besides that, you should pay attention to the following principles when implementing your anonymizer:

• Challenges and submissions will not get any internet access when running on the AnoMed platform. Make your anonymizer self-containing.
• Explain your API well in the anonymizer description, such that custom deanonymizers have it easy to obey your API. Template deanonymizers are likely incompatible with your custom anonymizer.
• Provide a default route GET / which returns a JSON encoded message like "Anonymizer server is alive!" for diagnosis, upon request.
• Provide a route POST /fit which starts the the fitting process upon request. If fitting has been successful, respond with a 201 Created. If POST /fit is invoked while a fitting is already in progress, respond with 503 Service Unavailable.
• Provide a route POST /evaluate which triggers the evaluation of your anonymizer. Depending on the specific challenge, you might have to expect query parameters like data_split which differentiate the kind of evaluation (e.g. intermediate vs final evaluation).