A city government anonymizes traffic data by removing specific license plate numbers and generalizing vehicle types. However, the data still allows attackers to infer which areas are experiencing congestion based on patterns in vehicle types. Which risk is introduced by insufficient anonymization?

What happens to the original sensitive data once tokenization is applied?

Which of the following is true about the tokens used in tokenization?

Which privacy model addresses the ‘homogeneity attack’ problem in k-Anonymity?

What is the primary advantage of using dynamic data masking over traditional data encryption?

Consider the following dataset representing longitudinal health records of patients over 5 years:

To apply k-anonymity, which of the following modifications would be most effective?

An IoT-based smart home system records appliance usage patterns over time. To anonymize this data while preserving usability, what should be done?

Which privacy model is used to protect against background knowledge attacks in complex data structures?

Which privacy-preserving technique in transaction data allows for the replacement of sensitive data elements with pseudonyms, making them unidentifiable?

A fitness tracking company wants to anonymize users’ step count data recorded every minute. Attackers could re-identify users based on unique activity patterns. What is the best privacy-preserving technique?