Challenges in inferring privacy properties of smart devices
Aniketh Girish, Vijay Prakash, Serge Egelman, Joel Reardon, Juan Tapiador, Danny Yuxing Huang, Srdjan Matic, Narseo Vallina-Rodríguez · 2022
The growth of the number of Internet of Things (IoT) devices in the home has introduced unprecedented challenges for preserving consumers' privacy. To enhance the transparency and trustworthiness of IoT products, industry actors, regulators and standardization bodies have proposed several certifications processes. While previous research developed methodologies to expose a wide range of harmful behaviors in smart home devices, the research community has not yet produced scalable methods to exhaustively detect information leakage in heterogeneous environments so that they can be effectively used for independent certification processes. Research questions such as to what extent is it possible to automatically and independently validate high-level privacy properties and can we evaluate the correctness of privacy controls in a smart home by analyzing the home traffic? remain open. This paper explores and discusses open research challenges in this complex domain and sketches a roadmap to build scalable methods for smart home privacy testing.