报告题目：Privacy-preserving Average Consensus: Theory and Algorithm（隐私保护的平均一致性：理论与算法）
The goal of the privacy-preserving average consensus (PPAC) is to guarantee the privacy of initial states and asymptotic consensus on the exact average of the initial value. This goal is achieved by an existing PPAC algorithm by adding and subtracting variance decaying and zero-sum random noises to the consensus process. However, there is lack of theoretical analysis to quantify the degree of the privacy protection. In this talk, we analyze the privacy of the PPAC algorithm in the sense of the maximum disclosure probability that the other nodes can infer one node's initial state within a given small interval. We first introduce a privacy definition, named (ϵ,σ)-data-privacy, to depict the maximum disclosure probability. We prove that PPAC provides (ϵ,σ)-data-privacy, and obtain the closed-form expression of the relationship between ϵ and σ. We also prove that the added noise with uniform distribution is optimal in terms of achieving the highest (ϵ,σ)-data-privacy. Then, we prove that the disclosure probability will converge to one when all information used in the consensus process is available, i.e., the privacy is compromised. Finally, we propose an optimal privacy-preserving average consensus (OPAC) algorithm to achieve the highest (ϵ,σ)-data-privacy. Simulations are conducted to verify the results.
何建平，加拿大维多利亚大学电子与计算机工程系副研究员。2013年毕业于浙江大学控制科学与工程专业，获博士学位。研究方向主要包括信息物理系统（CPS）的控制和优化、车载自组网（VANET）和社会网络的调度和优化、金融市场和电力市场的投资决策等。他是KSII Transactions on Internet and Information Systems期刊的副主编，International Journal of Robust and Nonlinear Control、Neurocomputing、International Journal of Distributed Senor Networks等期刊的客座编辑，曾获2015年中国自动化学会优秀博士论文奖励。
报告题目：Defending Against False Data Injection Attacks on Power System State Estimation（电力系统状态估计中数据注入攻击的防御）
This talk investigates the problem of defending against false data injection (FDI) attacks on power system state estimation. Although many research works have been previously reported on addressing the same problem, yet most of them made a very strong assumption that some meter measurements can be absolutely protected. To address the problem practically, a reasonable approach is to assume whether or not a meter measurement could be compromised by an adversary does depend on the defense budget deployed by the defender on the meter. From this perspective, our contributions focus on designing the least-budget defense strategy to protect power systems against FDI attacks. In addition, we also extend to investigate choosing which meters to be protected and determining how much defense budget to be deployed on each of these meters. We further formulate the meter selection problem as a mixed integer nonlinear programming problem, which can be efficiently tackled by Benders’ Decomposition. Finally, extensive simulations are conducted on IEEE test power systems to demonstrate the advantages of the proposed approach in terms of computing time and solution quality, especially for large-scale power systems.
邓瑞龙，加拿大阿尔伯塔大学电气与计算机工程系博士后。2009和2014年毕业于浙江大学控制科学与工程专业，分别获得学士和博士学位。2011年在挪威Simula国家实验室做访问学者，2012至2013年在加拿大滑铁卢大学做访问学者，2014至2015年在新加坡南洋理工大学做研究员。研究方向包括智能电网、信息安全和无线传感器网络等。目前担任IEEE/KICS Journal of Communications and Networks期刊的编辑，以及IEEE Transactions on Emerging Topics in Computing和Journal of Computer Networks and Communications (Hindawi)期刊的客座编辑，IEEE GLOBECOM、IEEE ICC、IEEE SmartGridComm、EAI SGSC等会议的程序委员会委员。他还是IEEE PES-GM 2016最佳会议论文奖获得者，已有3篇论文入选ESI高被引论文。