It significantly reduces dataset granularity

It does not prevent attribute linkage attacks

It removes contextual meaning from categorical variables

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

k-Anonymity

 t-Closeness

l-Diversity

Differential Privacy

It allows attackers to infer missing data using background knowledge

It removes sensitive attributes completely

It makes differential privacy impossible

It increases the risk of data poisoning attacks

Frequency Attack

Membership Inference Attack

Homogeneity Attack

Linkage Attack

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

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

All sensitive attributes must be removed from the dataset

The dataset should be completely synthetic

Encrypting the dataset before publishing

Increasing dataset granularity

Applying k-Anonymity to group records with similar attributes

Using non-deterministic encryption

Tokenization is a reversible process, while encryption is not

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

Tokenization changes the original data format, while encryption does not

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

It can distort or change the statistical properties

It makes the data easier to analyze

It enhances the accuracy of statistical analysis

It does not affect statistical properties

Branch Location

Transaction History

Age Range & ZIP Code

Name & Account Number