Privacy-enhanced architecture for occupancy-based HVAC Control

Large-scale sensing and actuation infrastructures have allowed buildings to achieve significant energy savings; at the same time, these technologies introduce significant privacy risks that must be addressed. In this paper, we present a framework for modeling the trade-off between improved control p...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2017 ACM IEEE 8th International Conference on Cyber Physical Systems (ICCPS) s. 177 - 186
Hlavní autoři: Jia, Ruoxi, Dong, Roy, Sastry, S. Shankar, Spanos, Costas J.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 18.04.2017
Edice:ACM Other Conferences
Témata:
Buildings
Computing methodologies > Artificial intelligence > Control methods
Computing methodologies > Modeling and simulation > Model development and analysis > Modeling methodologies
Control systems
Data privacy
Distortion
Distortion measurement
Energy
HVAC
model predictive control
occupancy
optimization
Privacy
Security and privacy > Human and societal aspects of security and privacy > Privacy protections
model predictive control
privacy
HVAC
occupancy
optimization
energy
ISBN:9781450349659, 145034965X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Large-scale sensing and actuation infrastructures have allowed buildings to achieve significant energy savings; at the same time, these technologies introduce significant privacy risks that must be addressed. In this paper, we present a framework for modeling the trade-off between improved control performance and increased privacy risks due to occupancy sensing. More specifically, we consider occupancy-based HVAC control as the control objective and the location traces of individual occupants as the private variables. Previous studies have shown that individual location information can be inferred from occupancy measurements. To ensure privacy, we design an architecture that distorts the occupancy data in order to hide individual occupant location information while maintaining HVAC performance. Using mutual information between the individual's location trace and the reported occupancy measurement as a privacy metric, we are able to optimally design a scheme to minimize privacy risk subject to a control performance guarantee. We evaluate our framework using real-world occupancy data: first, we verify that our privacy metric accurately assesses the adversary's ability to infer private variables from the distorted sensor measurements; then, we show that control performance is maintained through simulations of building operations using these distorted occupancy readings.
ISBN:9781450349659
145034965X
DOI:10.1145/3055004.3055007