Analyzing the Impact and Accuracy of Facebook Activity on Facebook's Ad-Interest Inference Process

Abstract

The use of voice-control technology has become mainstream and is growing worldwide. While voice assistants provide convenience through automation and control of home appliances, the open nature of the voice channel makes voice assistants difficult to secure. As a result voice assistants have been shown to be vulnerable to replay attacks, impersonation attacks and inaudible voice commands. Existing defenses do not provide a practical solution as they either rely on external hardware (e.g., motion sensors) or work under very constrained settings (e.g., holding the device close to a user’s mouth). We introduce the concept of using a gesture-based authentication system for smart home voice assistants called HandLock, which uses built-in microphones and speakers to generate and sense inaudible acoustic signals to detect the presence of a known (i.e., authorized) hand gesture. Our proposed approach can act as a second-factor authentication (2-FA) for performing specific sensitive operations like confirming online purchases through voice assistants. Through extensive experiments involving 45 participants, we show that HandLock can achieve on average 96.51% true-positive-rate (TPR) at the expense of 0.82% false-acceptance-rate (FAR). We perform a comprehensive analysis of HandLock under various settings to showcase its accuracy, stability, resilience to attacks, and usability. Our analysis shows that HandLock can not only successfully thwart impersonation attacks, but can do so while incurring very low overheads and is compatible with modern voice assistants

Publication
In Proceedings of the 25th ACM Conference On Computer-Supported Cooperative Work And Social Computing