メインコンテンツへスキップ

ホーム Python

コース

Pythonで学ぶデータプライバシーと匿名化

上級スキルレベル

更新日 2022/06

プライバシー保護手法を用いて、機密情報を安全に処理する方法を学びましょう。

コースを無料で開始

PythonMachine Learning

4時間

16 ビデオ

49 演習

3,850 XP

3,757

修了証明書

何千もの企業の従業員が支持

チームのトレーニングを担当していますか？

Businessをお試しください

コース説明

データプライバシーの重要性はこれまでになく高まっています。一方で、価値あるビジネスインサイトを収集・共有するニーズとの両立はどうすればよいのでしょうか？このコースでは、GoogleやAmazonが用いるのと同じ手法――データの一般化や、k-Anonymity や Differential Privacy といったプライバシーモデル――を使って、そのバランスの取り方を学びます。GDPR などの話題に触れつつ、従業員情報や収入データのような機微情報を保護しながら、Python で Machine Learning モデルを構築・学習させる方法も身につけます。さあ、始めましょう！

前提条件

Unsupervised Learning in Python

1

Introduction to Data Privacy

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.

What's private, and why do we care?

Privacy is power

Is it sensitive or non-sensitive?

Suppression of sensitive attributes

Data masking and data generation with Faker

Masking sensitive PII

Removing names with faker

Anonymizing with data generalization

Reducing identification risk with generalization

Data aggregation and data generalization

Top and bottom coding White House salaries

チャプターを開始

2

More on Privacy-Preserving Techniques

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.

Anonymizing categorical data

Explore the distribution of data

Sampling from the same probability distribution

Anonymizing continuous data

Different distributions

Sampling from the best continuous distribution

Introduction to K-anonymity

Privacy attributes

Generalizing into ranges

Generalizing data using hierarchies

Using hierarchies for categorical data

K-anonymizing a dataset

チャプターを開始

3

Differential Privacy

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.

Introduction to differential privacy

Epsilon (ϵ): the magic number

Histograms with differential privacy

Privacy budgets

Using privacy budgets

When no budget is left

Exploring data with a privacy budget accountant

Differentially private machine learning models

Build a differentially private classifier

Predicting salaries

Differentially private clustering models

Pre-processing data

Segmenting customers

チャプターを開始

4

Anonymizing and Releasing Datasets

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.

PCA for anonymization

Anonymization of high-dimensional data

Data masking with PCA

Generating realistic datasets with Faker

Consistent synthetic dataset

Datasets with the same probabilistic distribution

Creating synthetic datasets using scikit-learn

Generating datasets for classification

Generating datasets for clustering

Safely release datasets to the public

Exploring and pseudonymizing a dataset

Preparing employee data for safe release

Great work!

チャプターを開始

Pythonで学ぶデータプライバシーと匿名化

コース完了

修了証明書を取得

この修了書をLinkedInや履歴書、CVに追加しましょう
ソーシャルメディアや人事評価で共有しましょう今すぐ登録

19百万人を超える学習者と共にPythonで学ぶデータプライバシーと匿名化を始めましょう！

DataCamp for Mobileでデータスキルを磨きましょう

モバイルコースと毎日の 5 分間のコーディングチャレンジで、外出先でも進歩できます。