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.

Highlights

  • The smart grid is a worldwide modernization of electrical power systems in the 21st century.Two-way communication networks enable smart grids to collect real-time data from both the electricity supply and demand sides, and further boost the power system’s reliability, availability, and efficiency.As an essential enabler and prerequisite of the smart grid, smart meters are being installed countryand world-wide at single houses to collect real-time data on energy consumption

  • An evaluation is implemented to discuss whether the proposed scheme satisfies requirements in Section 2 considering both functionality and privacy

  • The privacy measures are adopted to the smart meter data shared with stakeholders The privacyaggregated measures data are adopted to the smart meter data shared with to stakeholders (high(high-frequency and down-sampled individual data)

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Summary

Introduction

The smart grid is a worldwide modernization of electrical power systems in the 21st century. User demand shaping approaches modify electricity data using methods such as energy storage systems or rechargeable batteries in households [7] This requires the installation of extra devices, which is expensive. Covering all European countries, the purpose of GDPR is to protect all EU citizens from privacy and data violation, providing more power to individuals to control their personal information With these operational and legal operational possibilities, it is important to consider ‘soft’ ethical strategies that use them to contribute to protect household privacy, potentially enabling households to be more in control of their digital data [10]. The system follows an operational and ethically (consequentialist) driven trade-off strategy and model which could contribute to increase functionalities of current smart metering devices in smart grids whilst ensuring that digital privacy intrusion is minimised and protected if not appropriately governed. The conclusion, implications, and future work are drawn in the last sections of the paper

Smart Grids
Functions of the Smart Metering System
Billing
Grid Operation and Management
Value-Added Services
Time-of-Use Tariff
Privacy Intrusion Issues
Real-Time Surveillance
Non-Grid Commercial Uses of Data
Related Work for Privacy Intrusion Protection
Demand Shaping
Data Manipulation
A Proposed Privacy-Functionality Trade-off Strategy and Model
Compulsory Functions
Data Minimisation and Protection
The Smart Meter
Protection from Inner and Outer Attacks—Adversary Element
High-Frequency Aggregated Data Channel
Time-of-Use
Additional Service Channel
Evaluation
Privacy Measure
NILM Performance as a Privacy Measure
Differential Privacy as Privacy Guarantee
Dataset Description and Data Preprocessing
Influence
Influence of Aggregation on aggregated
The TOU Tariff Channel Satisfies Privacy Requirement
Privacy Measure of Algorithm Sensitivity
ES Can Verify Billing Correctness
Comparison of the Proposed
Conclusions
Implications for Policy
Future Work
Privacy-preserving
October

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