Bir veri kümesini keşfetme ve takma adlandırma

Bu egzersizde, öğrenci performansı veri kümesini inceleyecek ve belirli örnekleme anonimleştirme teknikleri için hangi sütunların daha uygun olduğuna karar vereceksin.

numpy zaten np olarak yüklendi ve veri kümesi students olarak yüklendi.

Bu egzersiz, kursun bir parçasıdır

Python ile Veri Gizliliği ve Anonimleştirme

Kursa Göz Atın

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# Print the summarized information of the DataFrame
print(____)

# Print the number of unique values in each column of students 
print(____)

Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

Python ile Veri Gizliliği ve Anonimleştirme

AvançadoNível de habilidade

4.9+

Kursa Ücretsiz Başla

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: 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: Anonimleştirme için PCA Exercise 2: Yüksek boyutlu verilerin anonimleştirilmesi Exercise 3: PCA ile veri maskeleme Exercise 4: Faker ile gerçekçi veri kümeleri oluşturma Exercise 5: Tutarlı sentetik veri kümesi Exercise 6: Aynı olasılıksal dağılıma sahip veri kümeleri Exercise 7: scikit-learn kullanarak sentetik veri kümeleri oluşturma Exercise 8: Sınıflandırma için veri kümeleri oluşturma Exercise 9: Kümeleme için veri kümeleri oluşturma Exercise 10: Veri kümelerini güvenle kamuya aç Exercise 11: Bir veri kümesini keşfetme ve takma adlandırma

Geçerli egzersiz

Exercise 12: Verilerin güvenli yayımlanması için çalışan verilerini hazırlama Exercise 13: Harika iş!