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Data Privacy and Anonymization in Python

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Updated 12/2024

Learn to process sensitive information with privacy-preserving techniques.

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PythonMachine Learning4 hours16 videos49 exercises3,850 XP3,003Statement of Accomplishment

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Course Description

Data privacy has never been more important. But how do you balance privacy with the need to gather and share valuable business insights? In this course, you'll learn how to do just that, using the same methods as Google and Amazon—including data generalization and privacy models, like k-Anonymity and differential privacy. In addition to touching on topics such as GDPR, you'll also discover how to build and train machine learning models in Python while protecting users’ sensitive information such as employee and income data. Let’s get started!

Prerequisites

Unsupervised Learning in Python

1

Introduction to Data Privacy

What's private, and why do we care?

Privacy is power

Is it sensitive or non-sensitive?

Suppression of sensitive attributes

Data masking and data generation with Faker

Masking sensitive PII

Removing names with faker

Anonymizing with data generalization

Reducing identification risk with generalization

Data aggregation and data generalization

Top and bottom coding White House salaries

2

More on Privacy-Preserving Techniques

Anonymizing categorical data

Explore the distribution of data

Sampling from the same probability distribution

Anonymizing continuous data

Different distributions

Sampling from the best continuous distribution

Introduction to K-anonymity

Privacy attributes

Generalizing into ranges

Generalizing data using hierarchies

Using hierarchies for categorical data

K-anonymizing a dataset

3

Differential Privacy

Introduction to differential privacy

Epsilon (ϵ): the magic number

Histograms with differential privacy

Privacy budgets

Using privacy budgets

When no budget is left

Exploring data with a privacy budget accountant

Differentially private machine learning models

Build a differentially private classifier

Predicting salaries

Differentially private clustering models

Pre-processing data

Segmenting customers

4

Anonymizing and Releasing Datasets

PCA for anonymization

Anonymization of high-dimensional data

Data masking with PCA

Generating realistic datasets with Faker

Consistent synthetic dataset

Datasets with the same probabilistic distribution

Creating synthetic datasets using scikit-learn

Generating datasets for classification

Generating datasets for clustering

Safely release datasets to the public

Exploring and pseudonymizing a dataset

Preparing employee data for safe release

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Data Privacy and Anonymization in Python

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