Private-kNN: Practical Differential Privacy for Computer Vision
CVPR 2020 | The Private Aggregation of Teacher Ensembles (PATE) approach requires the training sets for the teachers to be disjoint. As such, achieving desirable privacy bounds requires an often impractical amount of labeled data. We propose a data-efficient scheme, which altogether avoids splitting the training dataset. Our approach allows the use of privacy amplification by subsampling and iterative refinement of the kNN feature embedding. Comparing to PATE, we achieve comparable or better utility while reducing more than 90% privacy cost, thereby providing the “most practical method to date” in computer vision.
Collaborators: Yuqing Zhu, Manmohan Chandraker, Yu-Xiang Wang