It removes contextual meaning from categorical variables

It does not prevent attribute linkage attacks

It significantly reduces dataset granularity

It prevents data utility for analysis

 It makes linkage attacks easier

It requires more computational power than k-Anonymity

 It increases the risk of membership inference attacks

It does not prevent attribute disclosure when values are unequally distributed

Differential Privacy

 t-Closeness

k-Anonymity

l-Diversity

It allows attackers to infer missing data using background knowledge

It makes differential privacy impossible

It removes sensitive attributes completely

It increases the risk of data poisoning attacks

Frequency Attack

Membership Inference Attack

Linkage Attack

Homogeneity Attack

All sensitive attributes must be removed from the dataset

Each sensitive attribute value must appear an equal number of times in the dataset

The dataset should be completely synthetic

The distribution of a sensitive attribute in each equivalence class must be close to the overall dataset’s distribution

Encrypting the dataset before publishing

Applying k-Anonymity to group records with similar attributes

Increasing dataset granularity

Using non-deterministic encryption

Tokenization changes the original data format, while encryption does not

Encryption requires a cryptographic key to decrypt, while tokenization does not

Tokenization makes data fully unreadable, whereas encryption allows data to be recovered

Tokenization is a reversible process, while encryption is not

It does not affect statistical properties

It makes the data easier to analyze

It enhances the accuracy of statistical analysis

It can distort or change the statistical properties

Removing all user data after processing

Adding security measures after data collection

Allowing unrestricted data sharing

Ensuring privacy is a default setting in systems and processes