Identificatierisico verminderen met generalisatie

In deze oefening pas je generalisatie toe op de IBM HR Analytics Employee Attrition & Performance-gegevensset.

Specifieker ga je de variabele monthly_income omzetten naar een binaire kolom. De drempel voor deze transformatie is de naar boven afgeronde gemiddelde waarde van de salarissen. Nieuwe waarden worden 0 voor bedragen die kleiner dan of gelijk aan het gehele gemiddelde zijn, en 1 voor bedragen die groter zijn.

De gegevensset is ingeladen als een pandas DataFrame hr.

Deze oefening maakt deel uit van de cursus

Dataprivacy en anonimisering in Python

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Oefeninstructies

Bereken de gemiddelde waarde van de kolom monthly_income met .mean() en rond deze af naar een geheel getal. Sla dit op als mean_income.
Pas een lambda-functie toe op hr['monthly_income'] om de inkomens te generaliseren naar 0 voor waarden die kleiner dan of gelijk aan mean_income zijn, en 1 voor waarden die groter zijn.
Bekijk de eerste vijf rijen van de resulterende DataFrame hr.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Calculate the mean value of incomes
mean_income = ____

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

# See resulting DataFrame
print(____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Dataprivacy en anonimisering in Python

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Begin de cursus gratis

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: Wat is privé, en waarom is dat belangrijk?Exercise 2: Privacy is macht Exercise 3: Is het gevoelig of niet-gevoelig?Exercise 4: Onderdrukking van gevoelige attributen Exercise 5: Gegevensmaskering en gegevensgeneratie met Faker Exercise 6: Maskeren van gevoelige PII Exercise 7: Namen verwijderen met faker Exercise 8: Anonymiseren met gegevensgeneralisatie Exercise 9: Identificatierisico verminderen met generalisatie

Huidige oefening

Exercise 10: Gegevensaggregatie en gegevensgeneralisatie Exercise 11: Top- en bottom-coding van salarissen in het Witte Huis

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!