Genelleştirme ile kimlik tespit riskini azaltma

Bu egzersizde, IBM HR Analytics Employee Attrition & Performance veri kümesi üzerinde genelleştirme uygulayacaksın.

Daha özel olarak, monthly_income değişkenini ikili (binary) bir sütuna dönüştüreceksin. Dönüşüm için kullanılacak eşik değeri, maaşların ortalamasının yukarı yuvarlanmış hali olacak. Yeni değerler, tam sayı ortalamadan küçük veya ona eşit olanlar için 0, daha büyük olanlar için 1 olacak.

Veri kümesi pandas DataFrame'i hr olarak yüklendi.

Bu egzersiz

Python ile Veri Gizliliği ve Anonimleştirme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

.mean() kullanarak monthly_income sütununun ortalamasını hesapla ve tam sayıya yuvarla. mean_income olarak kaydet.
Gelirleri genelleştirmek için hr['monthly_income'] üzerine bir lambda fonksiyonu uygula: değeri mean_income'dan küçük veya eşitse 0, büyükse 1 yap.
Ortaya çıkan hr DataFrame'inin ilk beş satırını incele.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Calculate the mean value of incomes
mean_income = ____

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

# See resulting DataFrame
print(____)

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: Özel olan nedir ve neden önemseriz?Exercise 2: Gizlilik güçtür Exercise 3: Hassas mı, hassas olmayan mı?Exercise 4: Hassas özniteliklerin bastırılması Exercise 5: Faker ile veri maskeleme ve veri üretimi Exercise 6: Hassas PII’yi maskeleme Exercise 7: faker ile isimleri kaldırma Exercise 8: Veri genelleştirme ile anonimleştirme Exercise 9: Genelleştirme ile kimlik tespit riskini azaltma

Geçerli Egzersiz

Exercise 10: Veri toplulaştırma ve veri genelleştirme Exercise 11: Beyaz Saray maaşlarında üst ve alt kodlama

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!