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Blockchain-Enabled Decentralized Privacy-Preserving Group Purchasing for Energy Plans

Sid Chi-Kin Chau, Yue Zhou

TL;DR

This paper tackles privacy and transparency challenges in group purchasing of energy plans by proposing a decentralized framework that fuses privacy-preserving blockchain, secure multi-party computation, and zero-knowledge proofs. It formalizes the decision problem as a Metrical Task System and applies a Work Function Algorithm with mutual compensation schemes to coordinate group switches without exposing private inputs. A SPDZ-based protocol provides privacy-preserving online decision-making and on-chain validation of encrypted receipts and compensations, with Merkle-tree commitments and Pedersen commitments enabling efficient, verifiable data handling on Ethereum-like platforms. Empirical results show that mutual compensations significantly improve adoption speed and yield notable cost savings, while SPDZ and on-chain gas costs scale linearly with the number of participants, illustrating practical feasibility and potential applicability to other group-purchasing contexts.

Abstract

Retail energy markets are increasingly consumer-oriented, thanks to a growing number of energy plans offered by a plethora of energy suppliers, retailers and intermediaries. To maximize the benefits of competitive retail energy markets, group purchasing is an emerging paradigm that aggregates consumers' purchasing power by coordinating switch decisions to specific energy providers for discounted energy plans. Traditionally, group purchasing is mediated by a trusted third-party, which suffers from the lack of privacy and transparency. In this paper, we introduce a novel paradigm of decentralized privacy-preserving group purchasing, empowered by privacy-preserving blockchain and secure multi-party computation, to enable users to form a coalition for coordinated switch decisions in a decentralized manner, without a trusted third-party. The coordinated switch decisions are determined by a competitive online algorithm, based on users' private consumption data and current energy plan tariffs. Remarkably, no private user consumption data will be revealed to others in the online decision-making process, which is carried out in a transparently verifiable manner to eliminate frauds from dishonest users and supports fair mutual compensations by sharing the switching costs to incentivize group purchasing. We implemented our decentralized group purchasing solution as a smart contract on Solidity-supported blockchain platform (e.g., Ethereum), and provide extensive empirical evaluation.

Blockchain-Enabled Decentralized Privacy-Preserving Group Purchasing for Energy Plans

TL;DR

This paper tackles privacy and transparency challenges in group purchasing of energy plans by proposing a decentralized framework that fuses privacy-preserving blockchain, secure multi-party computation, and zero-knowledge proofs. It formalizes the decision problem as a Metrical Task System and applies a Work Function Algorithm with mutual compensation schemes to coordinate group switches without exposing private inputs. A SPDZ-based protocol provides privacy-preserving online decision-making and on-chain validation of encrypted receipts and compensations, with Merkle-tree commitments and Pedersen commitments enabling efficient, verifiable data handling on Ethereum-like platforms. Empirical results show that mutual compensations significantly improve adoption speed and yield notable cost savings, while SPDZ and on-chain gas costs scale linearly with the number of participants, illustrating practical feasibility and potential applicability to other group-purchasing contexts.

Abstract

Retail energy markets are increasingly consumer-oriented, thanks to a growing number of energy plans offered by a plethora of energy suppliers, retailers and intermediaries. To maximize the benefits of competitive retail energy markets, group purchasing is an emerging paradigm that aggregates consumers' purchasing power by coordinating switch decisions to specific energy providers for discounted energy plans. Traditionally, group purchasing is mediated by a trusted third-party, which suffers from the lack of privacy and transparency. In this paper, we introduce a novel paradigm of decentralized privacy-preserving group purchasing, empowered by privacy-preserving blockchain and secure multi-party computation, to enable users to form a coalition for coordinated switch decisions in a decentralized manner, without a trusted third-party. The coordinated switch decisions are determined by a competitive online algorithm, based on users' private consumption data and current energy plan tariffs. Remarkably, no private user consumption data will be revealed to others in the online decision-making process, which is carried out in a transparently verifiable manner to eliminate frauds from dishonest users and supports fair mutual compensations by sharing the switching costs to incentivize group purchasing. We implemented our decentralized group purchasing solution as a smart contract on Solidity-supported blockchain platform (e.g., Ethereum), and provide extensive empirical evaluation.
Paper Structure (42 sections, 9 equations, 6 figures, 2 tables, 12 algorithms)

This paper contains 42 sections, 9 equations, 6 figures, 2 tables, 12 algorithms.

Table of Contents

  1. Introduction
  2. Decision-Making Model
  3. Problem Setup
  4. Standalone Online Energy Plan Selection
  5. Group-based Online Energy Plan Selection
  6. Mutual Compensations for Group Purchasing
  7. Group Purchasing Decision-Making Mechanism
  8. Security & Threat Model
  9. Security and Privacy Requirements
  10. Threat Model
  11. System Model
  12. Blockchain Model
  13. Energy Bill Data
  14. Cryptographic Commitments
  15. Merkel Trees
  16. ...and 27 more sections

Figures (6)

  • Figure 1: An illustration of our solution for decentralized privacy-preserving group purchasing.
  • Figure 2: A flowchart of the group purchasing decision-making mechanism.
  • Figure 3: An example of a given Merkle tree and a proof of membership for ${\tt Cm}(a)$ in the Merkle tree is $({\tt Hash_1}, {\tt Hash_{23}})$, such that ${\tt Root} = {\mathcal{H}}( {\mathcal{H}}( {\mathcal{H}}({\tt Cm}(a))\mid {\tt Hash_1}) \mid {\tt Hash_{23}})$
  • Figure 4: Evaluation of online decision algorithm, comparing the online solutions with and without mutual compensations.
  • Figure 5: SPDZ performance and smart contract gas costs
  • ...and 1 more figures