A membership inference attack is the process of determining whether a sample comes from the training dataset of an ML model. In the case of data synthetization, a membership attack is successful if the attacker can identify from the generated synthetic data set whether the subject was part of the original data set. This is the leakage of sensitive information that an attacker could use against the individual. Private synthetic data should not be vulnerable to a membership attack.