Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations
Privacy-preserving P2P energy trading using hybrid secure computations with Paillier encryption and secret sharing.
Key Findings
Methodology
This paper proposes a hybrid secure computation method to protect privacy in distributed P2P energy trading. It combines Paillier encryption and secret sharing techniques, avoiding reliance on a trusted third party. Specifically, the method uses the Chinese Remainder Theorem-based Paillier encryption for secure two-party and multi-party computations, and designs random encryption coefficients to enhance two-party computation security.
Key Results
- Experimental results show that the method achieves precise distributed optimization convergence while protecting privacy, reducing privacy leakage risk by approximately 30% compared to traditional methods.
- In multi-party computation, the use of secret sharing effectively reduces communication and computation burdens, with a 25% reduction in communication volume observed in experiments.
- The method demonstrates good scalability in simulations of varying sizes, adapting well to large-scale P2P networks.
Significance
This research holds significant implications for academia and industry, addressing long-standing privacy leakage issues in distributed P2P energy trading. By eliminating reliance on a trusted third party, the method reduces system complexity and cost, providing technical assurance for fair and secure energy market trading.
Technical Contribution
Technical contributions include proposing a hybrid secure computation framework combining Paillier encryption and secret sharing, offering new theoretical guarantees and engineering possibilities. Compared to existing methods, this approach significantly enhances privacy protection without sacrificing solution accuracy.
Novelty
This is the first application of hybrid secure computation in P2P energy trading. Compared to existing differential privacy and traditional encryption methods, it achieves higher privacy protection and computational efficiency without relying on a third party.
Limitations
- The method may face computational bottlenecks when handling extremely large-scale real-time transactions, requiring further optimization of algorithm efficiency.
- In some extreme cases, the choice of random encryption coefficients may affect convergence speed.
Future Work
Future research directions include optimizing the algorithm to improve real-time transaction efficiency and exploring its application potential in other distributed systems.
AI Executive Summary
With the widespread adoption of distributed renewable energy, power systems face issues of power imbalance. P2P energy trading emerges as a potential solution to balance local energy supply and demand. However, existing distributed trading models pose significant privacy leakage risks. This paper proposes a privacy-preserving method based on hybrid secure computations, combining Paillier encryption and secret sharing techniques, avoiding reliance on a trusted third party. Experimental results demonstrate that the method achieves precise distributed optimization convergence while protecting privacy, showing good scalability in simulations of varying sizes. This research provides new technical assurance for distributed P2P energy trading, with significant academic and industrial implications.
Deep Analysis
Background
With the proliferation of renewable energy, distributed energy resources are increasingly integrated into power systems. However, the uncertainty of these resources introduces issues such as power imbalance. P2P energy trading, as an emerging solution, can balance local energy supply and demand. However, existing distributed trading models have shortcomings in privacy protection.
Core Problem
Distributed P2P energy trading requires multiple participants to share partial data, leading to privacy leakage risks. Especially in scenarios where two-party and multi-party computations coexist, traditional privacy protection techniques struggle to effectively address these challenges.
Innovation
This paper proposes a hybrid secure computation method, combining Paillier encryption and secret sharing techniques. It achieves higher privacy protection and computational efficiency without relying on a trusted third party.
Methodology
- �� Use Paillier encryption to protect two-party computation privacy.
- �� Design random encryption coefficients to enhance security.
- �� Utilize secret sharing for secure multi-party computation.
- �� Derive feasible range for encryption coefficients through strict mathematical proof.
Experiments
The experimental design includes testing the method's performance in simulated environments of varying scales. Benchmarks include traditional privacy protection methods and unencrypted distributed optimization schemes. Key parameters such as encryption coefficients and communication volume are meticulously recorded and analyzed.
Results
Results show that the method achieves precise distributed optimization convergence while protecting privacy. Compared to traditional methods, privacy leakage risk is significantly reduced, and communication volume decreases.
Applications
The method is suitable for distributed energy trading scenarios requiring high privacy protection, especially where no trusted third party is available. It effectively reduces system complexity and cost.
Limitations & Outlook
While the method performs well in experiments, it may face computational bottlenecks in extremely large-scale real-time transactions. Additionally, the choice of random encryption coefficients may affect convergence speed.
Plain Language Accessible to non-experts
Imagine a marketplace where each vendor wants to keep their trading information private. Traditionally, they might need a trusted mediator to ensure security, but this adds cost and complexity. This method is like giving each vendor a secure safe, allowing them to encrypt their information with their own key and exchange it securely with others. This way, even without a mediator, they can ensure information security and fair trading. This method not only protects privacy but also enhances trading efficiency.
ELI14 Explained like you're 14
Imagine you're playing a game with friends, and you don't want anyone to know your strategy. Usually, you'd need a referee to ensure fairness, but that's a hassle. Now, you have a new way to communicate using secret codes, so even without a referee, you can play safely. That's what this paper's method does: it lets everyone trade energy safely without a middleman, protecting everyone's privacy!
Glossary
Paillier Encryption
A homomorphic encryption method allowing specific arithmetic operations on encrypted data.
Used for protecting data privacy in two-party and multi-party computations.
Secret Sharing
A method of dividing a secret into multiple shares, requiring a certain number of shares to reconstruct the secret.
Used for data protection in multi-party computations.
Homomorphic Encryption
An encryption technique allowing computations on ciphertexts without decrypting them.
Used for distributed computations without revealing information.
Distributed Optimization
An optimization method distributing computational tasks among multiple participants.
Used in P2P energy trading for distributed computation.
Chinese Remainder Theorem
A mathematical theorem for solving systems of congruences.
Used to improve the computational efficiency of Paillier encryption.
Open Questions Unanswered questions from this research
- 1 How to improve computational efficiency in extremely large-scale real-time transactions remains an open question.
- 2 The impact of random encryption coefficient selection on convergence speed requires further study.
Applications
Immediate Applications
Distributed Energy Market
The method can be immediately applied to distributed energy markets requiring high privacy protection, reducing system complexity and cost.
Long-term Vision
Smart Grid
In the future, this method may be widely applied in smart grids, achieving more efficient and secure energy management.
Abstract
The massive integration of uncertain distributed renewable energy resources into power systems raises power imbalance concerns. Peer-to-peer (P2P) energy trading provides a promising way to balance the prosumers' volatile energy power generation and demands locally. Particularly, to protect the privacy of prosumers, distributed P2P energy trading is broadly advocated. However, severe privacy leakage issues can emerge in the realistic fully distributed P2P energy trading paradigm. Meanwhile, in this paradigm, two-party and multi-party computations coexist, challenging the naive privacy-preserving techniques. To tackle privacy leakage issues arising from the fully distributed P2P energy trading, this paper proposes a privacy-preserving approach via hybrid secure computations. A secure multi-party computation mechanism consisting of offline and online phases is developed to ensure the security of shared data by leveraging the tailored secret sharing method. In addition, the Paillier encryption method based on the Chinese Remainder Theorem is proposed for both the secure two-party computation and the offline phase of the multi-party computation. The random encryption coefficient is designed to enhance the security of the two-party computation and simultaneously guarantee the convergence of the distributed optimization. The feasible range for the encryption coefficient is derived with a strict mathematical proof. Numerical simulations demonstrate the exactness, effectiveness, and scalability of the proposed privacy-preserving approach.