VindiCo: Privacy Safeguard Against Adaptation Based Spyware in Human-in-the-Loop IoT

TL;DR

VindiCo is a novel detection and mitigation system designed to protect user privacy against a new category of context-aware spyware in IoT devices, effectively reducing spyware inference accuracy to baseline levels.

Contribution

The paper introduces VindiCo, a new information-based detection engine and mitigation techniques tailored for unknown spyware without prior signatures.

Findings

SpyCon can predict user behavior with 90.3% accuracy.

VindiCo reduces SpyCon's inference accuracy to baseline levels.

VindiCo operates with negligible overhead.

Abstract

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.