Sampling dari distribusi probabilitas yang sama

Banyak organisasi, seperti U.S. Census, merilis sampel data yang mereka kumpulkan tentang warga secara publik. Himpunan data ini terlebih dahulu dianonimkan menggunakan berbagai teknik, lalu sebagian kecil (sekitar 1% hingga 5%) dari sampel dirilis untuk memungkinkan perhitungan. Teknik sampling diketahui dapat mempertahankan karakteristik statistik data, sehingga orang dapat mempelajari dan memahami populasi yang mendasarinya.

Dalam latihan ini, Anda akan menganonimkan kolom department dari himpunan data IBM HR dengan melakukan sampling berdasarkan distribusi pada himpunan data asli.

Himpunan data telah dimuat sebagai hr.

Latihan ini adalah bagian dari kursus

Privasi Data dan Anonimisasi di Python

Petunjuk latihan

Dapatkan frekuensi relatif dari setiap nilai unik pada kolom department.
Ekstrak probabilitas dari counts dan simpan dalam variabel bernama distributions.
Lakukan sampling dari distribusi probabilitas yang telah dihitung sebelumnya. Ukuran sampel harus sama dengan ukuran himpunan data hr.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Obtain the probability distribution counts 
counts = ____

# Get the probability distribution values 
distributions = ____

# Sample from the calculated probability distributions
hr['department'] = np.random.choice(counts.index, 
                                    p=____, 
                                    size=len(____))

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

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: 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: Menganonimkan data kategorikal Exercise 2: Jelajahi distribusi data Exercise 3: Sampling dari distribusi probabilitas yang sama

Latihan Saat Ini

Exercise 4: Anonimisasi data kontinu Exercise 5: Sebaran yang berbeda Exercise 6: Pengambilan sampel dari distribusi kontinu terbaik Exercise 7: Pengenalan K-anonymity Exercise 8: Atribut privasi Exercise 9: Generalisasi menjadi rentang Exercise 10: Menggeneralisasi data menggunakan hierarki Exercise 11: Menggunakan hierarki untuk data kategorikal Exercise 12: Menerapkan k-anonymity pada himpunan data

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!