In a modern smart building, many aspects of the use can be monitored using sensing technologies. This enables a high number of data-driven applications used amongst others things for indoor comfort, energy efficiency, and space utilization. Therefore, the stakeholders of building operations can benefit greatly from these technologies and sensor data. Also, sharing some of similar sensor data from other parties as open data can enable a more robust data-driven application for optimizing building operations. To enable such data sharing effort, there is a need for performing a privacy risk assessment for analyzing the inherent potential ethical and privacy risks that can be posed for occupants and the organization operating in the building. Furthermore, it is increasingly difficult to identify the inference capabilities of modern machine learning methods e.g. for estimating occupancy from CO2 datasets. Also, recent studies have shown that the state-of-the-practice does not sufficiently protect some of the shared datasets. In this paper, we design and implement an ontology-based tool-chain that can be used as part of the privacy assessment to identify potential privacy risks. This tool-chain takes in a model of the dataset that is being considered for sharing and it creates a privacy risk report. Furthermore, the paper presents a privacy risk ontology which can be used to model known inference and privacy risks with their associated spatial and temporal requirements. We evaluate the tool-chain using five real-world datasets by comparing the analysis with the data custodian. The results obtained show that the tool-chain can identifies more risks, than a human data curator and thus, there is a need for tool support to perform these privacy risk analysis.
@inproceedings{schwee2020tool,
title={Tool-chain for supporting Privacy Risk Assessments},
author={Schwee, Jens Hjort and Sangogboye, Fisayo Caleb and Salim, Flora D and Kj{\ae}rgaard, Mikkel Baun},
booktitle={Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
pages={140--149},
year={2020}
}
© 2021 Flora Salim - CRUISE Research Group.