In this paper, we propose a novel Privacy-Preserving clearance mechanism for Local Energy Markets (PP-LEM), designed for computational efficiency and social welfare. PP-LEM incorporates a novel competitive game-theoretical clearance mechanism, modelled as a Stackelberg Game. Based on this mechanism, a privacy-preserving market model is developed using a partially homomorphic cryptosystem, allowing buyers' reaction function calculations to be executed over encrypted data without exposing sensitive information of both buyers and sellers. The comprehensive performance evaluation demonstrates that PP-LEM is highly effective in delivering an incentive clearance mechanism with computational efficiency, enabling it to clear the market for 200 users within the order of seconds while concurrently protecting user privacy. Compared to the state of the art, PP-LEM achieves improved computational efficiency without compromising social welfare while still providing user privacy protection.

翻译：本文提出了一种新颖的面向本地能源市场的隐私保护出清机制（PP-LEM），该机制专为计算效率与社会福利而设计。PP-LEM引入了一种基于竞争博弈论的新型出清机制，该机制被建模为一个斯塔克尔伯格博弈。基于此机制，我们利用部分同态加密系统构建了一个隐私保护市场模型，使得买方反应函数的计算可以在加密数据上执行，而无需暴露买卖双方的敏感信息。全面的性能评估表明，PP-LEM在提供具有计算效率的激励性出清机制方面非常有效，能够在秒级时间内为200名用户完成市场出清，同时保护用户隐私。与现有技术相比，PP-LEM在不损害社会福利且仍提供用户隐私保护的前提下，实现了更高的计算效率。