Steekproeven trekken uit de best passende continue verdeling

Willekeurig steekproeven trekken uit een goed passende kansverdeling helpt de privacy te behouden. Tegelijkertijd kunnen bevoegde partijen zo een nauwkeurige statistische analyse van de data uitvoeren.

In deze oefening ga je de kolom monthly_income uit de IBM-gegevensset anonimiseren. In de vorige les heb je bepaald dat de continue verdeling exponnorm het beste past. Gebruik die om de inkomens te modelleren.

De gegevensset is beschikbaar als hr.

Deze oefening maakt deel uit van de cursus

Dataprivacy en anonimisering in Python

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Oefeninstructies

Importeer de module stats uit het pakket scipy.
Pas de exponnorm-verdeling op de continue variabele monthly_income toe om de parameters van de verdeling te verkrijgen en later de steekproeven te genereren.
Trek steekproeven uit de exponnorm-verdeling en vervang monthly_income met de .rvs()-methode. Stel de size in op dezelfde waarde als de lengte van de kolom.
Rond de salarissen af op het dichtstbijzijnde gehele getal.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import stats from scipy
____

# Fit the exponnorm distribution to the continuous variable monthly income
params = ____

# Sample from the exponnorm distribution and replace monthly income
hr['monthly_income'] = ____

# Round the salaries to their closest integer
hr['monthly_income'] = ____

# See the resulting dataset
print(hr.head())

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: 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: Categorische gegevens anonimiseren Exercise 2: Onderzoek de verdeling van gegevens Exercise 3: Steekproeven trekken uit dezelfde kansverdeling Exercise 4: Continue gegevens anonimiseren Exercise 5: Verschillende verdelingen Exercise 6: Steekproeven trekken uit de best passende continue verdeling

Huidige oefening

Exercise 7: Introductie tot k-anonimiteit Exercise 8: Privacy-attributen Exercise 9: Generaliseren naar bereiken Exercise 10: Data generaliseren met behulp van hiërarchieën Exercise 11: Hiërarchieën gebruiken voor categorische data Exercise 12: K-anonimiseren van een gegevensset

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!