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Challenges in inferring privacy properties of smart devices: Towards scalable multi-vantage point testing methods

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conext22-sw-#23.pdf (794.3Kb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1642
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Autor(es)
Girish, Aniketh; Prakash, Vijay; Egelman, Serge; Reardon, Joel; Tapiador, Juan; Yuxing Huang, Danny; Matic, Srdjan; Vallina-Rodriguez, Narseo
Fecha
2022-12
Resumen
The growth of the number of Internet of Things (IoT) devices in the home has introduced unprecedented challenges for preserving con- sumers’ privacy. To enhance the transparency and trustworthiness of IoT products, industry actors, regulators and standardization bodies have proposed several certifications processes. While previ- ous 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 infor- mation 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.
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Ficheros
conext22-sw-#23.pdf (794.3Kb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1642
Metadatos
Mostrar el registro completo del ítem

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