BloomXNOR-Net: privacy-preserving machine learning in IoT

Abstract

In recent years, the Internet of Things (IoT) has become a dominant data generation framework for establishing a higher level of system intelligence. At the same time, to avail the full advantage of this domain, the adopters of IoT are also keen on applying Machine Learning (ML) algorithms to these datasets to reveal new business insights. However, these datasets contain sensitive information that demands careful processing to prevent privacy breaches. Many existing privacy-preserving ML (PPML) algorithms are unsuitable for these resource-constrained devices. We propose a novel PPML technique that can be executed on IoT devices using the Bloom Filter encoded IoT dataset in XNOR-Net architecture. The preliminary experimental result using the MNIST dataset shows satisfactory performance.