A homogoneity attack is a privacy attack that can be applied to data that is anonymized using a simple generalization technique if the data share the same values of their quasi-identifiers and have the same values for their sensitive attributes. If the groups do not contain different values, an attacker can reveal sensitive information simply by finding out which group an individual belongs to (if every individual in the group is diagnosed with a heart attack, it is easy for an attacker to find out the diagnosis of the individual who belongs to that group). In such cases, the data becomes vulnerable to a homogeneity attack. k-anonymity is not a sufficient prevention against this attack, the l-diversity method is intended to protect data from these types of attacks.