Mengurangi risiko identifikasi dengan generalisasi

Dalam latihan ini, Anda akan menerapkan generalisasi pada himpunan data IBM HR Analytics Employee Attrition & Performance.

Secara lebih spesifik, Anda akan mengubah variabel monthly_income menjadi kolom biner. Ambang yang digunakan untuk transformasi adalah nilai rata-rata gaji yang dibulatkan ke atas. Nilai baru akan 0 untuk yang kurang dari atau sama dengan rata-rata bilangan bulat tersebut, dan 1 untuk yang lebih besar.

Himpunan data telah dimuat sebagai DataFrame pandas bernama hr.

Latihan ini adalah bagian dari kursus

Privasi Data dan Anonimisasi di Python

Petunjuk latihan

Hitung nilai rata-rata kolom monthly_income menggunakan .mean() dan bulatkan menjadi bilangan bulat. Simpan sebagai mean_income.
Terapkan fungsi lambda ke hr['monthly_income'] untuk menggeneralisasi pendapatan menjadi 0 untuk nilai yang kurang dari atau sama dengan mean_income, dan 1 untuk nilai yang lebih besar.
Telusuri lima baris pertama dari DataFrame hr yang dihasilkan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate the mean value of incomes
mean_income = ____

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

# See resulting DataFrame
print(____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Privasi Data dan Anonimisasi di Python

SkillTag.level.advancedSkillTag.label

4.9+

Mulai Kursus 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: Apa yang bersifat privat, dan mengapa kita peduli?Exercise 2: Privasi adalah kekuatan Exercise 3: Apakah sensitif atau tidak sensitif?Exercise 4: Supresi atribut sensitif Exercise 5: Penyamaran data dan pembuatan data dengan Faker Exercise 6: Masking PII sensitif Exercise 7: Menghapus nama dengan faker Exercise 8: Anonimisasi dengan generalisasi data Exercise 9: Mengurangi risiko identifikasi dengan generalisasi

Latihan Saat Ini

Exercise 10: Agregasi data dan generalisasi data Exercise 11: Top dan bottom coding untuk gaji White House

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!