Ridurre il rischio di identificazione con la generalizzazione

In questo esercizio applicherai la generalizzazione al dataset IBM HR Analytics Employee Attrition & Performance.

In particolare, trasformerai la variabile monthly_income in una colonna binaria. La soglia da usare per la trasformazione sarà il valore medio degli stipendi arrotondato. I nuovi valori saranno 0 per quelli minori o uguali alla media intera e 1 per quelli maggiori.

Il dataset è caricato come pandas DataFrame hr.

Questo esercizio fa parte del corso

Riservatezza dei dati e anonimizzazione in Python

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Istruzioni dell'esercizio

Calcola il valore medio della colonna monthly_income usando .mean() e arrotondalo a un intero. Salvalo come mean_income.
Applica una funzione lambda a hr['monthly_income'] per generalizzare i redditi a 0 per i valori minori o uguali a mean_income e a 1 per quelli maggiori.
Esplora le prime cinque righe del DataFrame risultante hr.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Calculate the mean value of incomes
mean_income = ____

# Apply generalization by transforming to binary data
hr['monthly_income'] = ____

# See resulting DataFrame
print(____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Riservatezza dei dati e anonimizzazione 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: Cosa è privato e perché ci interessa?Exercise 2: La privacy è potere Exercise 3: È sensibile o non sensibile?Exercise 4: Soppressione di attributi sensibili Exercise 5: Data masking e generazione di dati con Faker Exercise 6: Mascherare PII sensibili Exercise 7: Rimozione dei nomi con faker Exercise 8: Anonimizzazione con la generalizzazione dei dati Exercise 9: Ridurre il rischio di identificazione con la generalizzazione

Esercizio in corso

Exercise 10: Aggregazione dei dati e generalizzazione dei dati Exercise 11: Top e bottom coding degli stipendi della Casa Bianca

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 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!