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Privacy-Friendly Peer-to-Peer Energy Trading: A Game Theoretical Approach
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abstract
In this paper, we propose a decentralized, privacy-friendly energy trading platform (PFET) based on game theoretical approach - specifically Stackelberg competition. Unlike existing trading schemes, PFET provides a competitive market in which prices and demands are determined based on competition, and computations are performed in a decentralized manner which does not rely on trusted third parties. It uses homomorphic encryption cryptosystem to encrypt sensitive information of buyers and sellers such as sellers$'$ prices and buyers$'$ demands. Buyers calculate total demand on particular seller using an encrypted data and sensitive buyer profile data is hidden from sellers. Hence, privacy of both sellers and buyers is preserved. Through privacy analysis and performance evaluation, we show that PFET preserves users$'$ privacy in an efficient manner.
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Cited by 1 Pith paper
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PP-LEM: Efficient and Privacy-Preserving Clearance Mechanism for Local Energy Markets
PP-LEM shows that a Paillier homomorphic encryption scheme with a special squaring step can clear a local energy market for 200 users in about half a second while hiding buyer and seller data.
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