Privacy-Aware Smart Metering: Progress and Challenges

Abstract

The next-generation energy network, the so-called smart grid (SG), promises a tremendous increase in efficiency, safety and flexibility of managing the electricity grid as compared to the legacy energy network. This is needed today more than ever, as the global energy consumption is growing at an unprecedented rate, and renewable energy sources have to be seamlessly integrated into the grid to assure a sustainable human development. Smart meters (SMs) are among the crucial enablers of the SG concept; they supply accurate high-frequency information about users' household energy consumption to a utility provider, which is essential for time of use pricing, rapid fault detection, energy theft prevention, while also providing consumers with more flexibility and control over their consumption. However, highly accurate and granular SM data also poses a threat to consumer privacy as non-intrusive load monitoring techniques enable a malicious attacker to infer many details of a user's private life. This article focuses on privacy-enhancing energy management techniques that provide accurate energy consumption information to the grid operator, without sacrificing consumer privacy. In particular, we focus on techniques that shape and modify the actual user energy consumption by means of physical resources, such as rechargeable batteries, renewable energy sources or demand shaping. A rigorous mathematical analysis of privacy is presented under various physical constraints on the available physical resources. Finally, open questions and challenges that need to be addressed to pave the way to the effective protection of users' privacy in future SGs are presented.

Submitted for publication

References in corpus (5)

• The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
• Increasing Smart Meter Privacy Through Energy Harvesting and Storage Devices
• Privacy-Protecting Energy Management Unit through Model-Distribution Predictive Control
• Smart Meter Privacy with an Energy Harvesting Device and Instantaneous Power Constraints
• Smart Meter Privacy via the Trapdoor Channel

Cited by in corpus (17)

• Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
• Real-Time Privacy-Preserving Data Release for Smart Meters
• Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning
• Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release
• Privacy-preserving smart meter control strategy including energy storage losses
• Consumer Privacy Protection using Flexible Thermal Loads: Theoretical Limits and Practical Considerations
• Market Value of Differentially-Private Smart Meter Data
• Learning Sparse Privacy-Preserving Representations for Smart Meters Data
• Energy Resource Control via Privacy Preserving Data
• On the Impact of Side Information on Smart Meter Privacy-Preserving Methods
• $\alpha$-Mutual Information: A Tunable Privacy Measure for Privacy Protection in Data Sharing
• Universal Mutual Information Privacy Guarantees for Smart Meters
• Privacy-cost trade-off in a smart meter system with a renewable energy source and a rechargeable battery
• Aggregating Privacy-Conscious Distributed Energy Resources for Grid Service Provision
• Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning
• Privacy against a Hypothesis Testing Adversary
• Load Shaping Based Privacy Protection in Smart Grids: An Overview