Differentially private distributed protocol for electric vehicle charging
Differentially private distributed protocol for electric vehicle charging
复制标题
电动汽车充电差分私有分布式协议
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
George Pappas
中科院分区：
文献类型：
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作者：
Shuo Han;U. Topcu;George Pappas
在分布式电动汽车 (EV) 充电中，通过交换所有充电站公开可用的协调信号，在中央服务器和充电站之间迭代解决优化问题。协调信号取决于充电站报告的用户需求，并且可能泄露充电站用户的私人信息。攻击者可以通过公共信号解码私人用户信息，并使用户隐私面临风险。本文开发了一种保留差分隐私的分布式电动汽车充电算法，这是最近在理论计算机科学中引入和研究的隐私概念。该算法基于所谓的拉普拉斯机制，用拉普拉斯噪声扰动公共信号，拉普拉斯噪声的大小由公共信号对用户信息变化的敏感度决定。论文推导了差分隐私计费算法的敏感性并分析了其次优性。特别是，我们通过将该算法视为随机梯度下降的实现来获得次优性的界限。最后，进行数值实验来研究算法在实际中使用时的各个方面，包括迭代次数以及隐私级别和次优性之间的权衡。
In distributed electric vehicle (EV) charging, an optimization problem is solved iteratively between a central server and the charging stations by exchanging coordination signals that are publicly available to all stations. The coordination signals depend on user demand reported by charging stations and may reveal private information of the users at the stations. From the public signals, an adversary can potentially decode private user information and put user privacy at risk. This paper develops a distributed EV charging algorithm that preserves differential privacy, which is a notion of privacy recently introduced and studied in theoretical computer science. The algorithm is based on the so-called Laplace mechanism, which perturbs the public signal with Laplace noise whose magnitude is determined by the sensitivity of the public signal with respect to changes in user information. The paper derives the sensitivity and analyzes the suboptimality of the differentially private charging algorithm. In particular, we obtain a bound on suboptimality by viewing the algorithm as an implementation of stochastic gradient descent. In the end, numerical experiments are performed to investigate various aspects of the algorithm when being used in practice, including the number of iterations and tradeoffs between privacy level and suboptimality.