This session includes three talks on the privacy issues of energy consumption data collected by smart meters. The first two talks are trying to protect the privacy of energy consumption data with different approaches while the third talk is to show how appliances activities in residential homes can be detected with the modeled energy consumption for different loads.
Talk 1:Shepherd: Sharing Energy for Privacy Preserving in Hybrid AC-DC Microgrids (paper link)
This work investigates the privacy issues in hybrid AC-DC microgrids, where both AC and DC transmission line exist and allow the energy sharing among buildings in the microgrids. Both adversarial models for a single home and microgrids with compromised buildings are proposed for privacy issues. An optimization problem is formulated to minimize the energy transmission in a microgrid while protecting the privacy for all the homes in the microgrid. The results show that the detection ratio with the proposed heuristic solution drops from 33% to 13% compared to battery based consumption hiding approaches.
Q: why there is privacy issue in the proposed setting?
The utility company tries to collect high granularity energy consumption in homes for control or billing purposes. However, they may sell the collected data to third party and the third party may utilize the data to analyze the activity patterns of people for advertisements.
Talk 2:Towards Provable Privacy Guarantees Using Rechargeable Energy-Storage Devices (paper link)
Different from the first talk, this talk is to utilize the rechargeable energy storage devices (e.g., batteries) to hide energy consumption information. The main idea is to change the energy consumption of a load by charging and discharging the battery. The goal of this work is to analyze the privacy guarantees for such strategies. Then a charging strategy is proposed to fulfill the requirement that the charging/discharging rates follow a generalized Irwin-Hall (GIH) distribution. The experiments show that the proposed strategy can give good privacy guarantees and outperforms conventional charging strategies.
Q: The batteries deployed are not supposed for protecting privacy, right? If the batteries is mainly used for protecting privacy, what about the initial purpose?
The work is to investigate the theoretical performance we can achieve if we utilize the battery for protecting the privacy. It can be further discussed how to balance between protecting privacy and other purposes for batteries.
This talk is to provide the insight of complex energy consumption patterns of different appliances in the residential buildings. The loads are categorized into four basic types: resistive, inductive, capacitive and non-linear and the corresponding consumption models for different appliances are proposed. The Non-Intrusive Model Derivation (NIMD) approach is presented for automated modeling of electrical loads: i) when the loads are turned on or off; ii) what are the usage patterns of different loads. The experimental evaluation showed that the automated models are within 1% of the ground truth.