To reduce the amount of data collected

To improve data accuracy

To increase data accessibility

To enhance data security

Aggregation

Generalization

Perturbation

Encryption

To remove personal identifiers from data

To improve data accuracy

To increase data accessibility

To enhance data security

Pseudonymization

K-anonymity

L-diversity

Differential privacy

Data minimization reduces the amount of data collected, while anonymization removes personal identifiers from data.

Data minimization improves data accuracy, while anonymization enhances data security.

Data minimization increases data accessibility, while anonymization restricts data access.

Data minimization is a legal requirement, while anonymization is a voluntary practice.

Reduced risk of data breaches

Improved data accuracy

Increased data accessibility

Enhanced data security

Increased data complexity

Loss of data utility

Difficulty in implementing anonymization techniques

All of the above

Collect only the data that is necessary for a specific purpose

Retain data only for as long as necessary

Use anonymization techniques to protect sensitive data

Store data in a secure location

Remove direct identifiers such as names and addresses

Use pseudonymization to replace personal identifiers with artificial identifiers

Apply generalization techniques to reduce the precision of data

Use differential privacy to add noise to data

Data governance establishes policies and procedures for data management

Data governance ensures compliance with data protection regulations

Data governance promotes a culture of data privacy and security

All of the above

General Data Protection Regulation (GDPR)

California Consumer Privacy Act (CCPA)

Health Insurance Portability and Accountability Act (HIPAA)

None of the above

By implementing data minimization and anonymization techniques that preserve the essential information required for analysis

By conducting thorough data analysis to identify the minimum amount of data needed for a specific purpose

By involving data users in the data minimization and anonymization process to ensure that their needs are met

All of the above

Federated learning for collaborative data analysis without sharing raw data

Synthetic data generation for creating realistic but anonymized datasets

Differential privacy algorithms for adding noise to data while preserving its statistical properties

All of the above

By attending industry conferences and workshops

By reading research papers and articles on data privacy and security

By participating in online forums and communities dedicated to data minimization and anonymization

All of the above

To protect the privacy of individuals

To ensure compliance with data protection regulations

To reduce the risk of data breaches

All of the above