Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning

Understand

In this work, we systematically explore the data privacy issues of dataset pruning in machine learning systems.

• Our findings reveal, for the first time, that even if data in the redundant set is solely used before model training, its pruning-phase membership status can still be detected through attacks.
• Since this is a fully upstream process before model training, traditional model output-based privacy inference methods are completely unsuitable.
• To address this, we introduce a new task called Data-Centric Membership Inference and propose the first ever data-centric privacy inference paradigm named Data Lineage Inference (DaLI).