Pre-elaborazione dei dati

La pre-elaborazione per il clustering può essere un modo per preparare i dati a una segmentazione più accurata. Un tipo di pre-elaborazione è il feature scaling, una tecnica che standardizza le feature indipendenti presenti nei dati per farle rientrare in un intervallo fisso, ad esempio 0-1 o 0-100.

In questo esercizio effettuerai il clustering sulle colonne parental_level_of_education e writing_score nel dataset delle performance degli studenti caricato come performance. Per prima cosa, creerai ed eseguirai un modello k-means senza alcuna pre-elaborazione. Poi ripeterai gli stessi passaggi, ma pre-elaborando i dati con il feature scaling.

Il modello k-means privato è stato importato come KMeans da diffprivlib.models. Lo scaler StandardScaler e la riduzione dimensionale PCA sono stati importati da sklearn.

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Riservatezza dei dati e anonimizzazione in Python

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Build the differentially private k-means model
model = KMeans(____)

# Fit the model to the data
____

# Print the inertia in the console output
print("The inertia of the private model is: ", model.inertia_)

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: 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: Introduzione alla differential privacy Exercise 2: Epsilon (ϵ): il numero magico Exercise 3: Istogrammi con privacy differenziale Exercise 4: Budget di privacy Exercise 5: Uso dei budget di privacy Exercise 6: Quando non resta budget Exercise 7: Esplorare i dati con un gestore del budget di privacy Exercise 8: Modelli di Machine Learning con privacy differenziale Exercise 9: Crea un classificatore con privacy differenziale Exercise 10: Prevedere gli stipendi Exercise 11: Modelli di clustering con differential privacy Exercise 12: Pre-elaborazione dei dati

Esercizio in corso

Exercise 13: Segmentazione dei clienti

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!