Privacy-Aware Distributed Graph-Based Semi-Supervised Learning

Basak Güler, Amir Salman Avestimehr, Antonio Ortega

Abstract: This paper proposes a privacy-aware framework for distributed semi-supervised learning. In particular, we consider a semi-supervised learning problem where the training data is distributed among multiple data-owners, who wish to protect the privacy of their individual datasets from the other parties during training. We propose a novel framework for protecting the privacy of individual datasets while achieving good accuracy. Then, we characterize the privacy of our framework, by defining a metric that quantifies the number of candidate data points that are consistent with information shared by data-owners. The number of candidates (and thus the privacy) decreases as more information is shared between data-owners, leading to a privacy-utility (accuracy) trade-off. Our experiments show a significant increase in classification accuracy compared to local training, i.e., using the individual datasets only, while the complexity of our approach is significantly lower than that of other benchmarks, such as secure multi-party computing or homomorphic encryption.