Privacy-Enhancing Context Authentication from Location-Sensitive Data
This addresses privacy concerns in context-aware authentication for mobile users, but is incremental as it applies existing LSH methods to a specific domain.
The paper tackled user authentication by transforming location-sensitive data into locality-sensitive hashing representations for privacy, and demonstrated its implementation on Android with evaluation from 35 users.
This paper proposes a new privacy-enhancing, context-aware user authentication system, ConSec, which uses a transformation of general location-sensitive data, such as GPS location, barometric altitude and noise levels, collected from the user's device, into a representation based on locality-sensitive hashing (LSH). The resulting hashes provide a dimensionality reduction of the underlying data, which we leverage to model users' behaviour for authentication using machine learning. We present how ConSec supports learning from categorical and numerical data, while addressing a number of on-device and network-based threats. ConSec is implemented subsequently for the Android platform and evaluated using data collected from 35 users, which is followed by a security and privacy analysis. We demonstrate that LSH presents a useful approach for context authentication from location-sensitive data without directly utilising plain measurements.