Published in ACM International Workshop on Smart Cities and Fog Computing (CitiFog), 2018
Occupancy modelling for efficient energy management of indoor spaces has gained significant recent attention. Unfortunately, many such models rely on copying sensor data to the cloud for third-party services to process, creating risks of privacy breach. Such matters have become particularly pertinent for companies handling data of EU citizens due to provisions of the General Data Protection Regulation (GDPR). In this paper we present an implementation of Occupancy-as-a-Service (OaaS) at the edge, inverting the usual model: rather than ship data to the cloud to be processed, we retain data where it is generated and compute on it locally. This effectively avoids many risks associated with moving personal data to the cloud, and increases the agency of data subjects in managing their personal data. We describe the Databox architecture, its core components, and the OaaS functionality. As well as improving the privacy of the occupants, our approach allows us to offer occupancy data to other applications running on Databox, at a granularity that is not constrained by network usage, storage or processing restrictions imposed by third-party services, but is under data subject control.
Recommended citation: Poonam Yadav, John Moore, Qi Li, Richard Mortier, Anthony Brown, Andy Crabtree, Chris Greenhalgh, Derek McAuley, Yousef Amar, Ali Shahin Shamsabadi, Hamed Haddadi. "Providing Occupancy as a Service with Databox." The 1st ACM International Workshop on Smart Cities and Fog Computing (CitiFog), November 4, 2018, Shenzhen, China. https://haddadi.github.io/papers/citifog18-final7.pdf