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.
We use third-party web analytics tools to analyze website usage and measure the success of advertising campaigns. Cookies are set in the process and data is partly transferred to the USA. Further details can be found in our privacy policy. You can revoke or adjust your selection at any time under Settings.
Here you will find an overview of all cookies used. You can give your consent to whole categories or display further information and select certain cookies.