Data Anonymization Techniques wk3 dpp

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Questions and Answers

What is the primary objective of data anonymization?

To protect individual privacy while retaining data usefulness (correct)
To make data more specific and identifiable
To completely delete all personal data
To assess the risk of re-identification

Which anonymization technique is considered the strongest type?

Data Obfuscation
Attribute Suppression (correct)
Character Masking
Data Encryption

What is the purpose of character masking in data anonymization?

To delete entire attributes
To hide part of a string of characters to provide anonymity (correct)
To replace entire data values with symbols
To assess the risk of re-identification

What is the characteristic of anonymized data?

It is typically irreversible (C) Signup and view all the answers

What is the number of anonymization techniques mentioned in the text?

9 (B) Signup and view all the answers

What is the purpose of assessing the risk of re-identification in anonymization?

To evaluate the effectiveness of anonymization (D) Signup and view all the answers

Study Notes

Data Anonymization

Data Anonymization is the process of converting personal data into anonymized data using various anonymization techniques.

Characteristics of Anonymized Data

Anonymized data is data that has undergone transformation through anonymization techniques.
It should be irreversible and non-reversible.
The goal is to modify data to protect individual privacy while retaining its usefulness.

Anonymization Techniques

Attribute Suppression

It involves completely deleting entire columns in a dataset.
This is the strongest type of anonymization technique.
Used when an attribute is no longer required in the anonymized dataset.

Character Masking

It involves changing characters of a data value using symbols like "*", "x", or "#".
Typically used on strings of characters.
Hides part of the data to provide anonymity, suitable for credit card numbers or postal codes (e.g., xxxx-xxxx-xxxx-4913).

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Description

Learn about the process of converting personal data into anonymized data using various techniques, ensuring data privacy while preserving data utility. Understand the importance of irreversible anonymization and assessing re-identification risks.