A Privacy-Preserving Framework for Cloud-Based HVAC Control

Abstract

The objective of this work is (i) to develop an encrypted cloud-based HVAC control framework to ensure the privacy of occupancy information, (ii) to reduce the communication and computation costs of encrypted HVAC control,(iii) to reduce the leakage of private information via the triggering time instances. Occupancy of a building is sensitive and private information that can be accurately inferred by cloud-based HVAC controllers. To ensure the privacy of the privacy information, in our framework, the measurements of an HVAC system are encrypted by a fully homomorphic encryption prior to communication with the cloud controller. We first develop an encrypted algorithm that allows the cloud controller to regulate the indoor temperature and CO_2 of a building. We next develop an event-triggered control policy to reduce the communication and computation costs of the encrypted HVAC control. We cast the optimal design of the event-triggered policy as an optimal control problem. Using Bellman's optimality principle, we study the structural properties of the optimal event-triggered policy and show the necessary information for optimal triggering policy. We also show that the optimal design of the event-triggered policy can be transformed into a Markov decision process by introducing new states. As the triggering time instances are not encrypted, there is a risk that the cloud may use them to deduce sensitive information. To mitigate this risk, we introduce two randomized triggering strategies. We finally study the performance of the developed encrypted HVAC control framework using the TRNSYS simulator. Our numerical results show that the proposed framework not only ensures efficient control of the indoor temperature and CO$_2$ but also reduces the computation and communication costs of encrypted HVAC control by at least 60%.