AnonML: Locally Private Machine Learning over a Network of Peers
Bennett Cyphers, Kalyan K. Veeramachaneni · 2017
We present AnonML, a system for privacy-preserving model generation over a network of peers. Our goal is to allow a group of users to combine enough data to generate useful machine learning models without revealing private information. In our setting, each peer has a single row of featurized data according to a shared schema, and an aggregator would like to train a binary classification model on the union of all peers' data. Our system horizontally and vertically partitions the set of all peers' data and assembles a differentially-private histogram for each partition. An ensemble classifier can then be trained on the set of noisy partitions. AnonML can be used with or without differentially private data perturbation. Without perturbation, the resulting classifiers achieve performance competitive with centrally-generated models. With local differential privacy, a strong theoretical guarantee, AnonML is capable of producing useful models for practical prediction problems.