Exploring data with a privacy budget accountant

Data exploration systems that provide differential privacy must manage a privacy budget that measures the amount of privacy lost across multiple queries.

In this exercise, you'll explore the IBM HR Analytics Employee Attrition & Performance dataset while keeping track of our privacy budget. Remember that if a query exceeds the privacy budget specified in the accountant, an error arises.

The histogram is a valuable tool to visualize the data in a differentially private way. The syntax is the same as the corresponding function in numpy, with an epsilon parameter.

The full dataset is available as hr and the employees' age attribute as ages. A custom function has been created and loaded as show_histogram() to plot the histogram as you did previously in the course.

Cet exercice fait partie du cours

Data Privacy and Anonymization in Python

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Instructions

Create a privacy BudgetAccountant with an epsilon of 1.5, using the constructor for it.
Generate a private histogram from the ages column and with an epsilon value of 0.1.
Get and show the private average of ages, using an epsilon of 0.9, and bounds from 10 to 100 as a tuple.
Print the privacy budget remaining for the two new following queries.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Create the privacy Budget Accountant with epsilon of 1.5
acc = ____

# Use the Budget Accountant acc to draw a private histogram of ages with epsilon 0.1
dp_hist, dp_bins = tools.___(____, epsilon=____, range=[10,100], accountant=____)
show_histogram(dp_hist, dp_bins)

# Get and show the private average of the age variable
print("Mean: ", tools.mean(____))

# Show privacy budget remaining for 2 queries
print("Remaining budget for 2 queries: ", ____)
print("Number of queries recorded: ", len(acc))

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

Exercice en cours

Exercise 8: Differentially private machine learning models Exercise 9: Build a differentially private classifier 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!