NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release

Understand

Privacy-preserving data release algorithms have gained increasing attention for their ability to protect user privacy while enabling downstream machine learning tasks.

• However, the utility of current popular algorithms is not always satisfactory.
• Mixup of raw data provides a new way of data augmentation, which can help improve utility.
• However, its performance drastically deteriorates when differential privacy (DP) noise is added.