The Internet of Things (IoT) is a booming trend in the current automation
industry and is estimated to cover a considerable share of the international market in
the near future. Sharing of IoT data, especially data with a potential risk of sensitive
data exposure, poses challenges to organizations. The rapid growth of IoT data is found
in diverse applications, and it is connected to different hardware and software
platforms. This leads to a major issue at the time of executing the application. Data
identification is made through anonymization procedures and de-identification
procedures before sharing with a third party. The detection of data points that have the
potential to expose sensitive information can be a tedious task, especially if done
manually. Automating the task helps make identification much easier when dealing
with a lot of data sets, both small and large. The current solution has been envisaged
with the intention of helping to identify potential leakage of sensitive information using
an easy-to-implement framework, as well as a solution for detecting potential quasiidentifiers in data.
Keywords: Anonymity, Automated detection, IoT security, IoT-centric data privacy, Sensitive IoT data.
