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  3. Data Privacy and Anonymization in Python
  • 1

    Introduction to Data Privacy

    Gratuito

    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.

  • 2

    More on Privacy-Preserving Techniques

    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.

  • 3

    Differential Privacy

    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.

  • 4

    Anonymizing and Releasing Datasets

    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.

Initializing

Exercicio

Sampling from the same probability distribution

Many organizations, such as the U.S. Census, publicly release samples of data that they collect about private citizens. These datasets are first anonymized using various techniques, and then a tiny fraction of 1% to 5% of a sample is released to enable calculations. Sampling is known to preserve the data's statistical characteristics, allowing people to study and understand the underlying population.

In this exercise, you will anonymize the column department of the IBM HR dataset by sampling from the original dataset's distributions.

The dataset has been loaded as hr.

Instruções

100 XP
  • Obtain the relative frequencies of each unique value in the department column.
  • Extract the probabilities from counts and store them in a variable called distributions.
  • Sample from the previously calculated probability distributions. The size of the sample should be the same as the size of the hr dataset.