Biblio

Grid modernization efforts with the latest information and communication technologies will significantly benefit smart grids in the coming years. More optical fibre communications between consumers and the control center will promise better demand response and customer engagement, yet the increasing attack surface and man-in-the-middle (MITM) threats can result in security and privacy challenges. Among the studies for more secure smart grid communications, quantum key distribution protocols (QKD) have emerged as a promising option. To bridge the theoretical advantages of quantum communication to its practical utilization, however, comprehensive investigations have to be conducted with realistic cyber-physical smart grid structures and scenarios. To facilitate research in this direction, this paper proposes an open-source, research-oriented co-simulation platform that orchestrates cyber and power simulators under the MOSAIK framework. The proposed platform allows flexible and realistic power flow-based co-simulation of quantum communications and electrical grids, where different cyber and power topologies, QKD protocols, and attack threats can be investigated. Using quantum-based communication under MITM attacks, the paper presented detailed case studies to demonstrate how the platform enables quick setup of a lowvoltage distribution grid, implementation of different protocols and cryptosystems, as well as evaluations of both communication efficiency and security against MITM attacks. The platform has been made available online to empower researchers in the modelling of quantum-based cyber-physical systems, pilot studies on quantum communications in smart grid, as well as improved attack resilience against malicious intruders.

It is a challenging problem to preserve the friendly-correlations between individuals when publishing social-network data. To alleviate this problem, uncertain graph has been presented recently. The main idea of uncertain graph is converting an original graph into an uncertain form, where the correlations between individuals is an associated probability. However, the existing methods of uncertain graph lack rigorous guarantees of privacy and rely on the assumption of adversary's knowledge. In this paper we first introduced a general model for constructing uncertain graphs. Then, we proposed an algorithm under the model which is based on differential privacy and made an analysis of algorithm's privacy. Our algorithm provides rigorous guarantees of privacy and against the background knowledge attack. Finally, the algorithm we proposed satisfied differential privacy and showed feasibility in the experiments. And then, we compare our algorithm with (k, ε)-obfuscation algorithm in terms of data utility, the importance of nodes for network in our algorithm is similar to (k, ε)-obfuscation algorithm.