Final answer:
k-anonymity is a data privacy concept the two main types of k-anonymity attacks are the Homogeneity Attack and the Background Knowledge Attack, both of which exploit different vulnerabilities to re-identify individuals in anonymized data sets.
Step-by-step explanation:
k-anonymity is a concept used in the field of data privacy. The objective of k-anonymity is to modify a data set so that each individual recorded is indistinguishable from at least k-1 other individuals within the data set. However, even anonymized data sets can be susceptible to various types of attacks which aim to reveal the identities of the individuals. There are two main types of k-anonymity attacks:
- Homogeneity Attack: This attack exploits the situation where all the records within a group (equivalence class) have the same value for a sensitive attribute. An attacker with background knowledge can infer the sensitive attribute value of an individual if all individuals in the group share that attribute value.
- Background Knowledge Attack: In this attack, an adversary uses external information to narrow down the identity of an individual. This can occur when the attacker already knows some information about the target and uses this in conjunction with the k-anonymized data to re-identify the individual.
Protecting against these attacks involves carefully considering the data transformation processes and evaluating the potential risks associated with publishing the anonymized data.