Context-Driven Granular Disclosure Control for Internet of Things Applications

Abstract

The Internet of Things (IoT) represents a technology revolution transforming the current environment into a ubiquitous world, whereby everything that benefits from being connected will be connected. Despite the benefits, the privacy of these things becomes a great concern and therefore it is imperative to apply privacy preservation techniques to IoT data collection. One such technique is called data obfuscation in which data is deliberately modified to blur the sensitive information, while preserving the data utility. The current obfuscation techniques, however, focus on the privacy of published datasets shared with untrusted parties. The high connectivity and distributed nature of IoT, opens up the possibility of privacy compromise before obfuscation can take effect, and therefore privacy enforcement should be deployed at earlier stages. Additionally, classical privacy treatments are too restrictive for IoT, where coarser/finer data details should be revealed for different applications. Motivated by these challenges, we propose a framework for privacy preservation in IoT environments that is capable of multi-granular obfuscation by enforcing context-driven disclosure policies. Then, we customize our framework for a smart vehicle system and make use of data stream watermarking techniques to protect privacy at different stages of the data lifecycle. To address possible concerns about additional performance overhead, we show the burden to be very lightweight, thus validating the suitability of ubiquitous use of our framework for IoT settings.