AnonML: Anonymous Machine Learning Over a Network of Data Holders

Bennett Cyphers, Kalyan Veeramachaneni

Centralized data warehouses can be disadvantageous to users for many reasons, including privacy, security, and control. We propose AnonML, a system for anonymous, peer-to-peer machine learning. At a high level, AnonML functions by moving as much computation as possible to its end users, away from a central authority. AnonML users store and compute features on their own data, thereby limiting the amount of information they need to share. To generate a model, a group of data-holding peers first agree on a model definition, a set of feature functions, and an aggregator, a peer who temporarily acts as a central authority. Each peer anonymously sends several small packets of labeled feature data to the aggregator. In exchange, the aggregator generates a classifier and shares it with the group. In this way, AnonML data holders control what information they share on a feature-by-feature and model-by-model basis, and peers are able to disassociate features from their digital identities. Additionally, each peer can generate models suited to their particular needs, and the whole network benefits from the creation of novel, useful models.