Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data

Understand

Synthetic data is emerging as one of the most promising solutions to share individual-level data while safeguarding privacy.

• While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset.
• This is often seen as a very strong assumption in practice, especially as the proposed main use cases for synthetic tabular data (e.g.
• medical data, financial transactions) are very specific and don't have any reference datasets directly available.