Providing Occupancy as a Service with Databox

ABSTRACT

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.

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Index Terms

• Computer systems organization

  • Embedded and cyber-physical systems

    • Sensors and actuators

• Security and privacy

  • Security services

    • Privacy-preserving protocols

  • Software and application security

    • Domain-specific security and privacy architectures