VindiCo: Privacy Safeguard Against Adaptation Based Spyware in Human-in-the-Loop IoT
This addresses privacy risks for users of IoT devices, presenting a novel solution to a previously unaddressed threat, though it is incremental in applying information-based detection to a specific domain.
The paper tackles the problem of privacy leakage in personalized IoT systems due to context-aware adaptation, showing that a new spyware category (SpyCon) can predict user behavior with 90.3% accuracy, and introduces VindiCo, a detection and mitigation mechanism that reduces this accuracy to baseline levels with negligible overhead.
Personalized IoT adapts their behavior based on contextual information, such as user behavior and location. Unfortunately, the fact that personalized IoT adapts to user context opens a side-channel that leaks private information about the user. To that end, we start by studying the extent to which a malicious eavesdropper can monitor the actions taken by an IoT system and extract users' private information. In particular, we show two concrete instantiations (in the context of mobile phones and smart homes) of a new category of spyware which we refer to as Context-Aware Adaptation Based Spyware (SpyCon). Experimental evaluations show that the developed SpyCon can predict users' daily behavior with an accuracy of 90.3%. The rest of this paper is devoted to introducing VindiCo, a software mechanism designed to detect and mitigate possible SpyCon. Being new spyware with no known prior signature or behavior, traditional spyware detection that is based on code signature or app behavior is not adequate to detect SpyCon. Therefore, VindiCo proposes a novel information-based detection engine along with several mitigation techniques to restrain the ability of the detected SpyCon to extract private information. By having general detection and mitigation engines, VindiCo is agnostic to the inference algorithm used by SpyCon. Our results show that VindiCo reduces the ability of SpyCon to infer user context from 90.3% to the baseline accuracy (accuracy based on random guesses) with negligible execution overhead.