PAD: Protecting Anonymity in Publishing Building Related Datasets

The diffusion of low-cost sensor network technologies in smart buildings has enabled the collection of massive amounts of data regarding indoor environments, energy use and occupants, which, in turn, creates opportunities for knowledge- and information-based building management. Driven by benefits mutual to occupants, building managers, and research communities, there is a demand for data publication to foster more sophisticated and robust models and algorithms. Data in the original form, however, contains sensitive information about occupants' behavioral patterns, and publishing such data will violate individuals' privacy. The current practice on publishing building-related datasets relies primarily on policies for dictating which types of data can be published and agreements on the use of published data. This approach alone provides insufficient protection as it does not prevent privacy breaches from occurring in the first place.

In this paper, we present PAD, which to our knowledge is the first system that provides a technological solution for publishing building related datasets in a privacy-preserving manner while maintaining high data quality. PAD is able to offer a strong anonymity guarantee by perturbing data records. The unique feature of PAD is that it offers an interface to incorporate dataset users into the loop of data publication and customizes the perturbation such that useful information in the dataset can be better retained. We study the efficacy of PAD using occupancy and plug load data collected in real buildings. The experiments demonstrate that PAD can achieve high resilience to privacy threats without introducing any significant data fidelity penalties.