Kund:innen segmentieren

In dieser Übung führst du eine Kundensegmentierung mit dem Mall Customer Segmentation Dataset durch – und zwar mit einem differentially privaten Clustering-Modell.

Beim K-Means-Clustering kannst du die optimale Anzahl an Clustern mit der Elbow-Methode bestimmen.

Ergebnisgrafik der Elbow-Methode mit nicht-privatem Modell

An der resultierenden Grafik erkennst du, dass die optimale Clusterzahl 5 ist. Du clustert nach Annual Income und Spending Score, die als X geladen wurden, und visualisierst die resultierenden Cluster.

Der vollständige Datensatz wurde als mall_df geladen. Der Einfachheit halber steht dir die benutzerdefinierte Funktion show_clusters() zum Plotten der Cluster zur Verfügung. Verwende ?show_clusters, um mehr darüber zu erfahren.

Diese Übung ist Teil des Kurses

<Kurs>Datenschutz und Anonymisierung mit Python</Kurs>

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Build the differentially private K-means model
model = ____

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Diese Übung ist Teil des Kurses

<Kurs>Datenschutz und Anonymisierung mit Python</Kurs>

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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: Einführung in Differential Privacy Exercise 2: Epsilon (ϵ): die magische Zahl Exercise 3: Histogramme mit Differential Privacy Exercise 4: Privacy Budgets Exercise 5: Verwendung von Privacy-Budgets Exercise 6: Wenn kein Budget mehr übrig ist Exercise 7: Daten erkunden mit einem Privacy-Budget-Accountant Exercise 8: Differenziell private Machine-Learning-Modelle Exercise 9: Erstelle einen differenziell privaten Klassifizierer Exercise 10: Gehälter vorhersagen Exercise 11: Differenziell private Clustering-Modelle Exercise 12: Daten vorverarbeiten Exercise 13: Kund:innen segmentieren

Aktuelle Übung

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!