Privacy-preserving building-related data publication using PAD

The massive data collected from buildings provide opportunities for data- and information-based building management. Furthermore, to benefit from collective efforts in research communities, there arises a need for methods to share building-related data in a privacy-preserving manner while being able to ensure the utility of published datasets. In this demo abstract, we present PAD, an open-sourced data publication system that offers k-anonymity guarantee. The novelty of this system is to incorporate data recipients' feedbacks into the publication process in order to improve data utility. We demonstrate the interface of PAD and highlight how participants (as data publishers) can generate sanitized datasets using this interface. Also, we demonstrate how participants (as data users) can provide feedback to PAD for improving data quality.