Build a differentially private classifier

In this exercise, you will build and train a private Gaussian Naive Bayes model on the Penguin dataset to classify if a penguin is male or female.

K-anonymity doesn't work well with high dimensional or diverse datasets due to its substantial theoretical and empirical limitations, the "curse of dimensionality". As the number of features or dimensions grows, the amount of data we need to generalize accurately grows exponentially. It's one of the reasons why differential privacy is the current preferred privacy model. Epsilon is independent of any background knowledge and "bounds" the sensitive information.

The DataFrame is loaded as penguin_df and split into X_train, y_train, X_test and y_test. The private model class has been imported as dp_GaussianNB.

Cet exercice fait partie du cours

Data Privacy and Anonymization in Python

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Instructions

Create a dp_GaussianNB classifier without parameters.
Fit the previously created model to the data without any parameters.
Calculate the score of the private model based on the test data.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Built the private classifier without parameters
dp_clf = ____

# Fit the model to the data
____(X_train, y_train)

# Print the accuracy score
print("The accuracy with default settings is ", ____(X_test, y_test))

Modifier et exécuter le code

Cet exercice fait partie du cours

Data Privacy and Anonymization in Python

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Get ready to apply anonymization techniques such as data suppression, masking, synthetic data generation, and generalization. In this chapter, you’ll learn how to distinguish between sensitive and non-sensitive personally identifiable information (PII), quasi-identifiers, and the basics of the GDPR. You'll also encounter real-life examples of what can go wrong if you don't follow these best practices.

Exercise 1: What's private, and why do we care?Exercise 2: Privacy is power Exercise 3: Is it sensitive or non-sensitive?Exercise 4: Suppression of sensitive attributes Exercise 5: Data masking and data generation with Faker Exercise 6: Masking sensitive PII Exercise 7: Removing names with faker Exercise 8: Anonymizing with data generalization Exercise 9: Reducing identification risk with generalization Exercise 10: Data aggregation and data generalization Exercise 11: Top and bottom coding White House salaries

Discover how to anonymize data by sampling from datasets following the probability distribution of the columns. You’ll then learn how to apply the k-anonymity privacy model to prevent linkage or re-identification attacks and use hierarchies to perform data generalization in categorical variables.

Exercise 1: Anonymizing categorical data Exercise 2: Explore the distribution of data Exercise 3: Sampling from the same probability distribution Exercise 4: Anonymizing continuous data Exercise 5: Different distributions Exercise 6: Sampling from the best continuous distribution Exercise 7: Introduction to K-anonymity Exercise 8: Privacy attributes Exercise 9: Generalizing into ranges Exercise 10: Generalizing data using hierarchies Exercise 11: Using hierarchies for categorical data Exercise 12: K-anonymizing a dataset

Learn about differential privacy, the model used by major technology companies such as Apple, Google, and Uber. In this chapter, you’ll explore data by generating private histograms and computing private averages in data. You’ll also create differentially private machine learning models that allow businesses to increase the utility of their data.

Exercise 1: Introduction to differential privacy Exercise 2: Epsilon (ϵ): the magic number Exercise 3: Histograms with differential privacy Exercise 4: Privacy budgets Exercise 5: Using privacy budgets Exercise 6: When no budget is left Exercise 7: Exploring data with a privacy budget accountant Exercise 8: Differentially private machine learning models Exercise 9: Build a differentially private classifier

Exercice en cours

Exercise 10: Predicting salaries Exercise 11: Differentially private clustering models Exercise 12: Pre-processing data Exercise 13: Segmenting customers

In this final chapter, you’ll learn how to apply dimensionality reduction methods such as principal component analysis (PCA) to anonymize large multi-column datasets. You’ll then use Faker to generate realistic and consistent datasets, and scikit-learn to create synthetic datasets that follow a normal distribution. Lastly, you’ll tie everything you learned in this course together as you combine multiple techniques to safely release datasets to the public.

Exercise 1: PCA for anonymization Exercise 2: Anonymization of high-dimensional data Exercise 3: Data masking with PCA Exercise 4: Generating realistic datasets with Faker Exercise 5: Consistent synthetic dataset Exercise 6: Datasets with the same probabilistic distribution Exercise 7: Creating synthetic datasets using scikit-learn Exercise 8: Generating datasets for classification Exercise 9: Generating datasets for clustering Exercise 10: Safely release datasets to the public Exercise 11: Exploring and pseudonymizing a dataset Exercise 12: Preparing employee data for safe release Exercise 13: Great work!