En iyi sürekli dağılımdan örnekleme

İyi uyan bir olasılık dağılımından rastgele örnekleme, mahremiyeti korumaya yardımcı olur. Aynı zamanda, yetkili kişilerin veri üzerinde doğru bir istatistiksel analiz yapmasına imkan tanır.

Bu egzersizde, IBM veri kümesindeki monthly_income sütununu anonimleştireceksin. Önceki derste, en iyi uyum sağlayanın sürekli exponnorm dağılımı olduğunu belirlemiştin. Gelirleri modellemek için bunu kullan.

Veri kümesi hr olarak mevcut.

Bu egzersiz

Python ile Veri Gizliliği ve Anonimleştirme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

scipy paketinden stats modülünü içe aktar.
Dağılımın parametrelerini elde etmek ve sonrasında örnek üretmek için sürekli monthly_income değişkenine exponnorm dağılımını uydur.
exponnorm dağılımından örnekle ve .rvs() metodunu kullanarak monthly_income değerlerinin yerine yaz. Boyutu, sütunun uzunluğuyla aynı olacak şekilde belirt.
Maaşları en yakın tam sayıya yuvarla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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())

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Veri Gizliliği ve Anonimleştirme

kursunun bir parçasıdır

AvançadoNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

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: Kategorik verileri anonimleştirme Exercise 2: Veri dağılımını keşfet Exercise 3: Aynı olasılık dağılımından örnekleme Exercise 4: Sürekli verileri anonimleştirme Exercise 5: Farklı dağılımlar Exercise 6: En iyi sürekli dağılımdan örnekleme

Geçerli Egzersiz

Exercise 7: K-anonimliğe giriş Exercise 8: Gizlilik öznitelikleri Exercise 9: Aralıklara genelleme Exercise 10: Hiyerarşiler kullanarak veriyi genelleştirme Exercise 11: Kategorik veriler için hiyerarşileri kullanma Exercise 12: Bir veri kümesini k-anonimleştirme

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!