Maskeren van gevoelige PII

Je hebt een gegevensset gekregen met Social Security-nummers (SSN) van Amerikaanse burgers, samen met hun woonplaats en leeftijd. Als deze ongepubliceerde gegevens alleen met toestemming met ons gedeeld mochten worden, zou het delen ervan een privacyschending opleveren, omdat je informatie openbaar maakt die de betrokkenen niet van ons verwachtten te delen.

Jouw taak is om de gegevens te anonimiseren door gedeeltelijke masking toe te passen op de gevoelige PII ssn. Onthoud: datamasking draait om het verbergen/obfusceren van gegevens om privacyschendingen te voorkomen, terwijl je het algemene formaat en de betekenis bewaart.

De gegevensset is geladen als insurance_df, maar sla de resulterende gegevens op in masked_df om het oorspronkelijke insurance_df intact te houden.

Deze oefening maakt deel uit van de cursus

Dataprivacy en anonimisering in Python

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Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Uniformly mask the SSN numbers with *
____

# See resulting DataFrame
print(____)

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Deze oefening maakt deel uit van de cursus

Dataprivacy en anonimisering in Python

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4.9+

Begin de cursus 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: Wat is privé, en waarom is dat belangrijk?Exercise 2: Privacy is macht Exercise 3: Is het gevoelig of niet-gevoelig?Exercise 4: Onderdrukking van gevoelige attributen Exercise 5: Gegevensmaskering en gegevensgeneratie met Faker Exercise 6: Maskeren van gevoelige PII

Huidige oefening

Exercise 7: Namen verwijderen met faker Exercise 8: Anonymiseren met gegevensgeneralisatie Exercise 9: Identificatierisico verminderen met generalisatie Exercise 10: Gegevensaggregatie en gegevensgeneralisatie Exercise 11: Top- en bottom-coding van salarissen in het Witte Huis

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!