Opposing Data Exploitation: Behaviour Biometrics for Privacy-Preserving Authentication in IoT Environments
Andraž Krašovec, Gianmarco Baldini, Veljko Pejović · 2021
Multimodal data harvested by the Internet of Things sensors has recently been utilised for behavioural biometrics and consequently user authentication. While strengthening the security, these data nevertheless present a privacy threat to users whose behaviour can now be modelled in detail, thus allowing the authenticating authority to not only know who is present, but also what the present person is doing. In this work we reconsider IoT sensor-based authentication and provide a solution mitigating the privacy risk associated with unnecessary information leaks. Our approach harnesses adversarial learning and identifies such a projection of the data that maintains identity separability, yet obfuscates activity separability, thus ensuring that the authenticating authority can successfully identify the user, but not her actions within the sensed environment. We evaluate our approach on a real-world dataset of three activities performed by fifteen users and show that the activity obfuscation is achieved without compromising identification capabilities.