Individual Property Inference Over Collaborative Learning in Deep Feature Space

Haoxin Yang, Yi Wang, Bin Li

Abstract: Collaborative learning is used in multi-media applications to distribute computing tasks and data storage over multiple sites. Recent studies found that private data information can be derived from model updates between the server and clients. Yet, previous methods are limited by their capabilities of privacy inference in more general and practical situations. In this paper, we propose a novel property inference method in the deep feature space to overcome those limitations. In particular, our method can make inference decisions on the level of individual examples instead of a batch of examples. We can simultaneously perform multiple property inference attacks without the need of image reconstruction. The proposed method is evaluated on several image benchmark datasets, which demonstrates significant improvement of inference accuracy even in the presence of privacy protection schemes.