Menghasilkan himpunan data untuk pengelompokan

Data sintetis sepenuhnya legal dan memenuhi semua persyaratan undang-undang serta regulasi privasi di seluruh dunia. Ini adalah alternatif yang valid dan berorientasi privasi terhadap data mentah. Fungsi make_blobs() dapat digunakan untuk menghasilkan titik data dengan sebaran Gaussian (atau normal).

Dalam latihan ini, Anda akan menghasilkan sebuah himpunan data berisi 15000 sampel.

numpy telah diimpor sebagai np, dan fungsi kustom plot_data_points() telah disediakan kembali untuk latihan ini.

Latihan ini adalah bagian dari kursus

Privasi Data dan Anonimisasi di Python

Petunjuk latihan

Impor fungsi yang sesuai dari modul datasets untuk menghasilkan himpunan data pengelompokan.
Hasilkan himpunan data berisi 15000 sampel dengan 2 fitur, 2 pusat, dan simpangan baku klaster sebesar 3.
Cetak bentuk (shape) data yang dihasilkan.
Periksa titik data yang dihasilkan dalam plot sebar 2 dimensi.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import the function from the datasets module for generating clustering datasets
from sklearn.datasets import ____

# Generate a dataset with 15000 rows, 2 features, 2 centers, and a cluster std of 3
x, labels = ____

# Print the shape of the resulting generated data
print(____)

# See the resulting data points in a 2 dimensional scatter plot
plot_data_points(x, labels)

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: 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 untuk anonimisasi Exercise 2: Anonimisasi data berdimensi tinggi Exercise 3: Penyamaran data dengan PCA Exercise 4: Membuat himpunan data realistis dengan Faker Exercise 5: Himpunan data sintetis yang konsisten Exercise 6: Himpunan data dengan distribusi probabilistik yang sama Exercise 7: Membuat himpunan data sintetis menggunakan scikit-learn Exercise 8: Menghasilkan himpunan data untuk klasifikasi Exercise 9: Menghasilkan himpunan data untuk pengelompokan

Latihan Saat Ini

Exercise 10: Merilis himpunan data ke publik secara aman Exercise 11: Mengeksplorasi dan membuat pseudonim pada himpunan data Exercise 12: Menyiapkan data karyawan untuk rilis yang aman Exercise 13: Kerja bagus!