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Privacy-Preserving Billing for Local Energy Markets

Eman Alqahtani, Mustafa A. Mustafa

TL;DR

This work tackles privacy-preserving billing in local energy markets by accounting for deviations from bids and incorporating grid-location based costs. It introduces PBP-LEM, a distributed protocol built on building blocks such as an efficient EPIBS, MPC, inner product functional encryption, and Pedersen commitments to ensure data confidentiality and correctness. It provides three privacy-performance approaches and proves security under UC framework, showing practical feasibility with large-scale deployments (e.g., 4,000 users in under five minutes for the most intensive setting and 0.18 seconds for the lightest) and open-source implementation. The results indicate that PBP-LEM can enable private, verifiable, and scalable billing in realistic LEMs, reducing privacy risks from internal collusion and avoiding reliance on a single trusted party.

Abstract

We propose a privacy-preserving billing protocol for local energy markets (PBP-LEM) that takes into account market participants' energy volume deviations from their bids. PBP-LEM enables a group of market entities to jointly compute participants' bills in a decentralized and privacy-preserving manner without sacrificing correctness. It also mitigates risks on individuals' privacy arising from any potential internal collusion. We first propose an efficient and privacy-preserving individual billing scheme, achieving information-theoretic security, which serves as a building block. PBP-LEM utilizes this scheme, along with other techniques such as multiparty computation, inner product functional encryption and Pedersen commitments to ensure data confidentiality and accuracy. Additionally, we present three approaches, resulting in different levels of privacy protection and performance. We prove that the protocol meets its security and privacy requirements and is feasible for deployment in real LEMs: bills can be computed in less than five minutes for 4,000 users using the most computationally intensive approach, and in just 0.18 seconds using the least intensive one.

Privacy-Preserving Billing for Local Energy Markets

TL;DR

This work tackles privacy-preserving billing in local energy markets by accounting for deviations from bids and incorporating grid-location based costs. It introduces PBP-LEM, a distributed protocol built on building blocks such as an efficient EPIBS, MPC, inner product functional encryption, and Pedersen commitments to ensure data confidentiality and correctness. It provides three privacy-performance approaches and proves security under UC framework, showing practical feasibility with large-scale deployments (e.g., 4,000 users in under five minutes for the most intensive setting and 0.18 seconds for the lightest) and open-source implementation. The results indicate that PBP-LEM can enable private, verifiable, and scalable billing in realistic LEMs, reducing privacy risks from internal collusion and avoiding reliance on a single trusted party.

Abstract

We propose a privacy-preserving billing protocol for local energy markets (PBP-LEM) that takes into account market participants' energy volume deviations from their bids. PBP-LEM enables a group of market entities to jointly compute participants' bills in a decentralized and privacy-preserving manner without sacrificing correctness. It also mitigates risks on individuals' privacy arising from any potential internal collusion. We first propose an efficient and privacy-preserving individual billing scheme, achieving information-theoretic security, which serves as a building block. PBP-LEM utilizes this scheme, along with other techniques such as multiparty computation, inner product functional encryption and Pedersen commitments to ensure data confidentiality and accuracy. Additionally, we present three approaches, resulting in different levels of privacy protection and performance. We prove that the protocol meets its security and privacy requirements and is feasible for deployment in real LEMs: bills can be computed in less than five minutes for 4,000 users using the most computationally intensive approach, and in just 0.18 seconds using the least intensive one.
Paper Structure (33 sections, 2 theorems, 12 equations, 7 figures, 3 tables, 5 algorithms)

This paper contains 33 sections, 2 theorems, 12 equations, 7 figures, 3 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model and Requirements
  4. System Model
  5. Threat Model and Assumptions
  6. Functional Requirements
  7. Security and Privacy Requirements
  8. Building Blocks
  9. Multiparty Computation
  10. Pedersen Commitments
  11. Inner Product Functional Encryption
  12. Inner product functional encryption (IPE)
  13. Dual Binary Encoding for Integer Comparison Using Inner Products
  14. Privacy-Preserving Billing Protocol for Local Energy Market
  15. Efficient and Privacy-Preserving Individual Billing Scheme
  16. ...and 18 more sections

Key Result

theorem 1

Let $\pi_{\text{EPIBS}}$ be the billing protocol employing EPIBS, excluding the consideration of the deviations cost. Then the protocol $\pi_{\text{EPIBS}}$ securely emulates the functionality $\mathcal{F}_{EPIBS}$.

Figures (7)

  • Figure 1: System model.
  • Figure 2: Functionality $\mathcal{F}_{EPIBS}$
  • Figure 3: Computational overhead for each entity per trading period.
  • Figure 4: Overall bills computation cost per trading period.
  • Figure 5: Total communication cost per trading period.
  • ...and 2 more figures

Theorems & Definitions (5)

  • definition 1: UC emulation Canetti
  • definition 2: Composition Theorem Canetti
  • theorem 1
  • definition 3: Ideal Functionality of Billing for LEM $\mathcal{F}_{BLEM}$
  • theorem 2