Privacy-Functionality Trade-Off: A Privacy-Preserving Multi-Channel Smart Metering System

Abstract

While smart meters can provide households with more autonomy regarding their energy consumption, they can also be a significant intrusion into the household’s privacy. There is abundant research implementing protection methods for different aspects (e.g., noise-adding and data aggregation, data down-sampling); while the private data are protected as sensitive information is hidden, some of the compulsory functions such as Time-of-use (TOU) billing or value-added services are sacrificed. Moreover, some methods, such as rechargeable batteries and homomorphic encryption, require an expensive energy storage system or central processor with high computation ability, which is unrealistic for mass roll-out. In this paper, we propose a privacy-preserving smart metering system which is a combination of existing data aggregation and data down-sampling mechanisms. The system takes an angle based on the ethical concerns about privacy and it implements a hybrid privacy-utility trade-off strategy, without sacrificing functionality. In the proposed system, the smart meter plays the role of assistant processor rather than information sender/receiver, and it enables three communication channels to transmit different temporal resolution data to protect privacy and allow freedom of choice: high frequency feed-level/substation-level data are adopted for grid operation and management purposes, low frequency household-level data are used for billing, and a privacy-preserving valued-add service channel to provide third party (TP) services. In the end of the paper, the privacy performance is evaluated to examine whether the proposed system satisfies the privacy and functionality requirements.

Privacy protection in smart meters using homomorphic encryption: An overview

“…Overall, existing methods to mitigate privacy issues with SM data can be divided into two broad categories: changing the energy consumption profile (CEP) or changing the smart‐meter data (CMD). Ultimately, all the techniques under these two categories attempt to minimize the loss of privacy by decreasing the chance of inferring sensitive information from the energy consumption data, for example, the consumption of individual appliances using NILM (Zhang et al, 2020).…”

A Hybrid Data-Driven Online Solar Energy Disaggregation System from the Grid Supply Point

“…The conventional method requires installing an electricity meter beside the solar panel of each house, which requires extra measurement devices as well as communication channels. These devices largely increase budgets and invade personal privacy [3]. Moreover, the ownership of these devices also raises conflicts among stakeholders.…”

Privacy boundary determination of smart meter data using an artificial intelligence adversary

“…Referring to Zhang et al, 17 the privacy intrusion issues raised by smart meters include data sensitivity and algorithm sensitivity. For data sensitivity, real‐time high‐resolution data (active/reactive power, voltage, time‐of‐use tariff, etc.)…”