Securing the Shared Resources of Machine Learning

Progress in machine learning depends on trust. Researchers often place their advances in a public well of shared resources, and developers draw on those to save enormous amounts of time and money. Coders use the code of others, harnessing common tools rather than reinventing the wheel. Engineers use systems developed by others as a basis for their own creations. Data scientists draw on large public datasets to train machines to carry out routine tasks, such as image recognition, autonomous driving, and text analysis. Machine learning has accelerated so quickly and proliferated so widely largely because of this shared well of tools and data.

But the trust that so many place in these common resources is a security weakness. Poison in this well can spread, affecting the products that draw from it. Right now, it is hard to verify that the well of machine learning is free from malicious interference. In fact, there are good reasons to be worried. Attackers can poison the well’s three main resources—machine learning tools, pretrained machine learning models, and datasets for training—in ways that are extremely difficult to detect. Read More