Keeping your mobile phone secure and protected is important to avoid identity theft and other security compromises. But phone passwords can be hard to remember, and current authentication methods are cumbersome.
One day you might not need passwords at all, thanks to research involving Google’s Advanced Technology and Projects team (ATAP) and University of Guelph researchers.
Engineering professor Graham Taylor and graduate student Griffin Lacey, along with Google’s ATAP, are developing a new phone security mechanism based on user habits.
“Mobile users find typing in passwords intrusive to their enjoyment of the phone,” says Lacey. “So we’re looking for a solution that could help improve the usability of their devices.”
As part of what Google ATAP called a “research sprint,” Lacey took part in ATAP’s Project Abacus for three months at Google’s California headquarters where he helped with a learning problem called continuous authentication, or guaranteeing user identity through multiple phone sensors such as touchscreen and keyboard input and images.
Google ATAP contacted Taylor as an expert on machine learning. This computational technique essentially teaches computers to learn like humans by finding patterns in large amounts of raw data.
In one form of machine learning called deep learning, a computer is taught to recognize patterns within multiple kinds of data — sound, images, keystrokes — and place them into categories. The device can then use the computer equivalent of a human’s neural pathways to distinguish between individuals.