Using Machine Learning to Address Customer Privacy Concerns: An Application with Click-stream Data

Abstract

The ever-increasing volume of consumer data provide unprecedented opportunities for firms to predict consumer behavior, target customers, and provide customized service. Recent trends of more restrictive privacy regulations worldwide, however, present great challenges for firms whose business activities rely on consumer data. We address these challenges by applying the recently developed federated learning approach --- a privacy-preserving machine learning approach that uses a parallelized learning algorithm to train a model locally on each individual user's device. We apply this approach to data from an online retailer and train a Gated Recurrent Unit recurrent neural network to predict each consumer's click-stream. We show the firm can predict each consumer's activities with a high level of accuracy without the need to store, access, or analyze consumer data in a centralized location, thereby protecting their sensitive information.

Keywords: Privacy, Machine Learning, Federated Learning, Gated Recurrent Unit, Click-Stream Data