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Zegers, Federico M., Hale, Matthew T., Shea, John M., Dixon, Warren E..  2020.  Reputation-Based Event-Triggered Formation Control and Leader Tracking with Resilience to Byzantine Adversaries. 2020 American Control Conference (ACC). :761—766.
A distributed event-triggered controller is developed for formation control and leader tracking (FCLT) with robustness to adversarial Byzantine agents for a class of heterogeneous multi-agent systems (MASs). A reputation-based strategy is developed for each agent to detect Byzantine agent behaviors within their neighbor set and then selectively disregard Byzantine state information. Selectively ignoring Byzantine agents results in time-varying discontinuous changes to the network topology. Nonsmooth dynamics also result from the use of the event-triggered strategy enabling intermittent communication. Nonsmooth Lyapunov methods are used to prove stability and FCLT of the MAS consisting of the remaining cooperative agents.
Sun, Weiqi, Li, Yuanlong, Shi, Liangren.  2020.  The Performance Evaluation and Resilience Analysis of Supply Chain Based on Logistics Network. 2020 39th Chinese Control Conference (CCC). :5772—5777.
With the development of globalization, more and more enterprises are involved in the supply chain network with increasingly complex structure. In this paper, enterprises and relations in the logistics network are abstracted as nodes and edges of the complex network. A graph model for a supply chain network to specified industry is constructed, and the Neo4j graph database is employed to store the graph data. This paper uses the theoretical research tool of complex network to model and analyze the supply chain, and designs a supply chain network evaluation system which include static and dynamic measurement indexes according to the statistical characteristics of complex network. In this paper both the static and dynamic resilience characteristics of the the constructed supply chain network are evaluated from the perspective of complex network. The numeric experimental simulations are conducted for validation. This research has practical and theoretical significance for enterprises to make strategies to improve the anti-risk capability of supply chain network based on logistics network information.
Bychkov, Igor, Feoktistov, Alexander, Gorsky, Sergey, Edelev, Alexei, Sidorov, Ivan, Kostromin, Roman, Fereferov, Evgeniy, Fedorov, Roman.  2020.  Supercomputer Engineering for Supporting Decision-making on Energy Systems Resilience. 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT). :1—6.
We propose a new approach to creating a subject-oriented distributed computing environment. Such an environment is used to support decision-making in solving relevant problems of ensuring energy systems resilience. The proposed approach is based on the idea of advancing and integrating the following important capabilities in supercomputer engineering: continuous integration, delivery, and deployment of the system and applied software, high-performance computing in heterogeneous environments, multi-agent intelligent computation planning and resource allocation, big data processing and geo-information servicing for subject information, including weakly structured data, and decision-making support. This combination of capabilities and their advancing are unique to the subject domain under consideration, which is related to combinatorial studying critical objects of energy systems. Evaluation of decision-making alternatives is carrying out through applying combinatorial modeling and multi-criteria selection rules. The Orlando Tools framework is used as the basis for an integrated software environment. It implements a flexible modular approach to the development of scientific applications (distributed applied software packages).
Guerrero-Bonilla, Luis, Saldaña, David, Kumar, Vijay.  2020.  Dense r-robust formations on lattices. 2020 IEEE International Conference on Robotics and Automation (ICRA). :6633—6639.
Robot networks are susceptible to fail under the presence of malicious or defective robots. Resilient networks in the literature require high connectivity and large communication ranges, leading to high energy consumption in the communication network. This paper presents robot formations with guaranteed resiliency that use smaller communication ranges than previous results in the literature. The formations can be built on triangular and square lattices in the plane, and cubic lattices in the three-dimensional space. We support our theoretical framework with simulations.
Shi, Jie, Foggo, Brandon, Kong, Xianghao, Cheng, Yuanbin, Yu, Nanpeng, Yamashita, Koji.  2020.  Online Event Detection in Synchrophasor Data with Graph Signal Processing. 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1—7.
Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.
Scarabaggio, Paolo, Carli, Raffaele, Dotoli, Mariagrazia.  2020.  A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). :1281—1286.
Traditionally, the management of power distribution networks relies on the centralized implementation of the optimal power flow and, in particular, the minimization of the generation cost and transmission losses. Nevertheless, the increasing penetration of both renewable energy sources and independent players such as ancillary service providers in modern networks have made this centralized framework inadequate. Against this background, we propose a noncooperative game-theoretic framework for optimally controlling energy storage systems (ESSs) in power distribution networks. Specifically, in this paper we address a power grid model that comprehends traditional loads, distributed generation sources and several independent energy storage providers, each owning an individual ESS. Through a rolling-horizon approach, the latter participate in the grid optimization process, aiming both at increasing the penetration of distributed generation and leveling the power injection from the transmission grid. Our framework incorporates not only economic factors but also grid stability aspects, including the power flow constraints. The paper fully describes the distribution grid model as well as the underlying market hypotheses and policies needed to force the energy storage providers to find a feasible equilibrium for the network. Numerical experiments based on the IEEE 33-bus system confirm the effectiveness and resiliency of the proposed framework.
Wang, Lei, Manchester, Ian R., Trumpf, Jochen, Shi, Guodong.  2020.  Initial-Value Privacy of Linear Dynamical Systems. 2020 59th IEEE Conference on Decision and Control (CDC). :3108—3113.
This paper studies initial-value privacy problems of linear dynamical systems. We consider a standard linear time-invariant system with random process and measurement noises. For such a system, eavesdroppers having access to system output trajectories may infer the system initial states, leading to initial-value privacy risks. When a finite number of output trajectories are eavesdropped, we consider a requirement that any guess about the initial values can be plausibly denied. When an infinite number of output trajectories are eavesdropped, we consider a requirement that the initial values should not be uniquely recoverable. In view of these two privacy requirements, we define differential initial-value privacy and intrinsic initial-value privacy, respectively, for the system as metrics of privacy risks. First of all, we prove that the intrinsic initial-value privacy is equivalent to unobservability, while the differential initial-value privacy can be achieved for a privacy budget depending on an extended observability matrix of the system and the covariance of the noises. Next, the inherent network nature of the considered linear system is explored, where each individual state corresponds to a node and the state and output matrices induce interaction and sensing graphs, leading to a network system. Under this network system perspective, we allow the initial states at some nodes to be public, and investigate the resulting intrinsic initial- value privacy of each individual node. We establish necessary and sufficient conditions for such individual node initial-value privacy, and also prove that the intrinsic initial-value privacy of individual nodes is generically determined by the network structure.
Sun, Mingjing, Zhao, Chengcheng, He, Jianping.  2020.  Privacy-Preserving Correlated Data Publication with a Noise Adding Mechanism. 2020 IEEE 16th International Conference on Control Automation (ICCA). :494—499.
The privacy issue in data publication is critical and has been extensively studied. However, most of the existing works assume the data to be published is independent, i.e., the correlation among data is neglected. The correlation is unavoidable in data publication, which universally manifests intrinsic correlations owing to social, behavioral, and genetic relationships. In this paper, we investigate the privacy concern of data publication where deterministic and probabilistic correlations are considered, respectively. Specifically, (ε,δ)-multi-dimensional data-privacy (MDDP) is proposed to quantify the correlated data privacy. It characterizes the disclosure probability of the published data being jointly estimated with the correlation under a given accuracy. Then, we explore the effects of deterministic correlations on privacy disclosure. For deterministic correlations, it is shown that the successful disclosure rate with correlations increases compared to the one without knowing the correlation. Meanwhile, a closed-form solution of the optimal disclosure probability and the strict bound of privacy disclosure gain are derived. Extensive simulations on a real dataset verify our analytical results.
Maswood, Mirza Mohd Shahriar, Uddin, Md Ashif, Dey, Uzzwal Kumar, Islam Mamun, Md Mainul, Akter, Moriom, Sonia, Shamima Sultana, Alharbi, Abdullah G..  2020.  A Novel Sensor Design to Sense Liquid Chemical Mixtures using Photonic Crystal Fiber to Achieve High Sensitivity and Low Confinement Losses. 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). :0686—0691.
Chemical sensing is an important issue in food, water, environment, biomedical, and pharmaceutical field. Conventional methods used in laboratory for sensing the chemical are costly, time consuming, and sometimes wastes significant amount of sample. Photonic Crystal Fiber (PCF) offers high compactness and design flexibility and it can be used as biosensor, chemical sensor, liquid sensor, temperature sensor, mechanical sensor, gas sensor, and so on. In this work, we designed PCF to sense different concentrations of different liquids by one PCF structure. We designed different structure for silica cladding hexagonal PCF to sense different concentrations of benzene-toluene and ethanol-water mixer. Core diameter, air hole diameter, and air hole diameter to lattice pitch ratio are varied to get the optimal result as well to explore the effect of core size, air hole size and the pitch on liquid chemical sensing. Performance of the chemical sensors was examined based on confinement loss and sensitivity. The performance of the sensor varied a lot and basically it depends not only on refractive index of the liquid but also on sensing wavelengths. Our designed sensor can provide comparatively high sensitivity and low confinement loss.
Lopes, Carmelo Riccardo, Zito, Pietro, Lampasi, Alessandro, Ala, Guido, Zizzo, Gaetano, Sanseverino, Eleonora Riva.  2020.  Conceptual Design and Modeling of Fast Discharge Unit for Quench Protection of Superconducting Toroidal Field Magnets of DTT. 2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON). :623—628.
The paper deals with the modelling and simulation of a Fast Discharge Unit (FDU) for quench protection of the Toroidal Field (TF) magnets of the Divertor Tokamak Test, an experimental facility under design and construction in Frascati (Italy). The FDU is a safety key component that protects the superconducting magnets when a quench is detected through the fast extraction of the energy stored in superconducting magnets by adding in the TF magnets a dump (or discharge) resistor. In the paper, two different configurations of dump resistors (fixed and variable respectively) have been analysed and discussed. As a first result, it is possible to underline that the configuration with variable dump resistor is more efficient than the one with a fixed dump resistor.
Shang, X., Shi, L.N., Niu, J.B., Xie, C.Q..  2020.  Efficient Mie Resonance of Metal-masked Titanium Dioxide Nanopillars. 2020 Fourteenth International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials). :171—173.
Here, we propose a simple design approach based on metal-masked titanium dioxide nanopillars, which can realize strong Mie resonance in metasurfaces and enables light confinement within itself over the range of visible wavelengths. By selecting the appropriate period and diameter of individual titanium dioxide nanopillars, the coincidence of resonance peak positions derived from excited electric and magnetic dipoles can be achived. And the optical properties in this design have been investigated with the Finite-Difference Time-Domain(FDTD) solutions.
Zhu, Luqi, Wang, Jin, Shi, Lianmin, Zhou, Jingya, Lu, Kejie, Wang, Jianping.  2020.  Secure Coded Matrix Multiplication Against Cooperative Attack in Edge Computing. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :547–556.
In recent years, the computation security of edge computing has been raised as a major concern since the edge devices are often distributed on the edge of the network, less trustworthy than cloud servers and have limited storage/ computation/ communication resources. Recently, coded computing has been proposed to protect the confidentiality of computing data under edge device's independent attack and minimize the total cost (resource consumption) of edge system. In this paper, for the cooperative attack, we design an efficient scheme to ensure the information-theory security (ITS) of user's data and further reduce the total cost of edge system. Specifically, we take matrix multiplication as an example, which is an important module appeared in many application operations. Moreover, we theoretically analyze the necessary and sufficient conditions for the existence of feasible scheme, prove the security and decodeability of the proposed scheme. We also prove the effectiveness of the proposed scheme through considerable simulation experiments. Compared with the existing schemes, the proposed scheme further reduces the total cost of edge system. The experiments also show a trade-off between storage and communication.
Thakare, Vaishali Ravindra, Singh, K. John, Prabhu, C S R, Priya, M..  2020.  Trust Evaluation Model for Cloud Security Using Fuzzy Theory. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). :1–4.
Cloud computing is a new kind of computing model which allows users to effectively rent virtualized computing resources on pay as you go model. It offers many advantages over traditional models in IT industries and healthcare as well. However, there is lack of trust between CSUs and CSPs to prevent the extensive implementation of cloud technologies amongst industries. Different models are developed to overcome the uncertainty and complexity between CSP and CSU regarding suitability. Several researchers focused on resource optimization, scheduling and service dependability in cloud computing by using fuzzy logic. But, data storage and security using fuzzy logic have been ignored. In this paper, a trust evaluation model is proposed for cloud computing security using fuzzy theory. Authors evaluates how fuzzy logic increases efficiency in trust evaluation. To validate the effectiveness of proposed FTEM, authors presents a case study of healthcare organization.
Hatti, Daneshwari I., Sutagundar, Ashok V..  2020.  Trust Induced Resource Provisioning (TIRP) Mechanism in IoT. 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP). :1–5.
Due to increased number of devices with limited resources in Internet of Things (IoT) has to serve time sensitive applications including health monitoring, emergency response, industrial applications and smart city etc. This has incurred the problem of solving the provisioning of limited computational resources of the devices to fulfill the requirement with reduced latency. With rapid increase of devices and heterogeneity characteristic the resource provisioning is crucial and leads to conflict of trusting among the devices requests. Trust is essential component in any context for communicating or sharing the resources in the network. The proposed work comprises of trusting and provisioning based on deadline. Trust quantity is measured with concept of game theory and optimal strategy decision among provider and customer and provision resources within deadline to execute the tasks is done by finding Nash equilibrium. Nash equilibrium (NE) is estimated by constructing the payoff matrix with choice of two player strategies. NE is obtained in the proposed work for the Trust- Respond (TR) strategy. The latency aware approach for avoiding resource contention due to limited resources of the edge devices, fog computing leverages the cloud services in a distributed way at the edge of the devices. The communication is established between edge devices-fog-cloud and provision of resources is performed based on scalar chain and Gang Plank theory of management to reduce latency and increase trust quantity. To test the performance of proposed work performance parameter considered are latency and computational time.
Zhang, Han, Song, Zhihua, Feng, Boyu, Zhou, Zhongliang, Liu, Fuxian.  2020.  Technology of Image Steganography and Steganalysis Based on Adversarial Training. 2020 16th International Conference on Computational Intelligence and Security (CIS). :77–80.
Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNN), which has caused severe problems in the network security field. Ensuring the accuracy of steganalysis is becoming increasingly difficult. In this paper, we designed a two-channel generative adversarial network (TGAN), inspired by the idea of adversarial training that is based on our previous work. The TGAN consisted of three parts: The first hiding network had two input channels and one output channel. For the second extraction network, the input was a hidden image embedded with the secret image. The third detecting network had two input channels and one output channel. Experimental results on two independent image data sets showed that the proposed TGAN performed well and had better detecting capability compared to other algorithms, thus having important theoretical significance and engineering value.
Xu, Lei, Gao, Zhimin, Fan, Xinxin, Chen, Lin, Kim, Hanyee, Suh, Taeweon, Shi, Weidong.  2020.  Blockchain Based End-to-End Tracking System for Distributed IoT Intelligence Application Security Enhancement. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1028–1035.
IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.
Sharma, Rajesh Kumar, Pippal, Ravi Singh.  2020.  Malicious Attack and Intrusion Prevention in IoT Network using Blockchain based Security Analysis. 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN). :380–385.
The Internet of Things (IoT) as a demanding technology require the best features of information security for effective development of the IoT based smart city and technological activity. There are huge number of recent security threats searching for some loopholes which are ready to exploit any network. Against the back-drop of recent rapidly growing technological advancement of IoT, security-threats have become a critical challenge which demand responsive and continuous action. As privacy and security exhibit an ever-present flourishing issue, so loopholes detection and analysis are indispensable process in the network. This paper presents Block chain based security analysis of data generated from IoT devices to prevent malicious attacks and intrusion in the IoT network.
Saigopal, Venkata Venugopal Rao Gudlur, Raju, Valliappan.  2020.  IIoT Digital Forensics and Major Security issues. 2020 International Conference on Computational Intelligence (ICCI). :233–236.
the significant area in the growing field of internet security and IIoT connectivity is the way that forensic investigators will conduct investigation process with devices connected to industrial sensors. This part of process is known as IIoT digital forensics and investigation. The main research on IIoT digital forensic investigation has been done, but the current investigation process has revealed and identified major security issues need to be addressed. In parallel, major security issues faced by traditional forensic investigators dealing with IIoT connectivity and data security. This paper address the issues of the challenges and major security issues identified by review conducted in the prospective and emphasizes on the aforementioned security and challenges.
Ghosal, Sandip, Shyamasundar, R. K..  2020.  A Generalized Notion of Non-interference for Flow Security of Sequential and Concurrent Programs. 2020 27th Asia-Pacific Software Engineering Conference (APSEC). :51–60.
For the last two decades, a wide spectrum of interpretations of non-interference11The notion of non-interference discussed in this paper enforces flow security in a program and is different from the concept of non-interference used for establishing functional correctness of parallel programs [1] have been used in the security analysis of programs, starting with the notion proposed by Goguen & Meseguer along with arguments of its impact on security practice. While the majority of works deal with sequential programs, several researchers have extended the notion of non-interference to enforce information flow-security in non-deterministic and concurrent programs. Major efforts of generalizations are based on (i) considering input sequences as a basic unit for input/output with semantic interpretation on a two-point information flow lattice, or (ii) typing of expressions as values for reading and writing, or (iii) typing of expressions along with its limited effects. Such approaches have limited compositionality and, thus, pose issues while extending these notions for concurrent programs. Further, in a general multi-point lattice, the notion of a public observer (or attacker) is not unique as it depends on the level of the attacker and the one attacked. In this paper, we first propose a compositional variant of non-interference for sequential systems that follow a general information flow lattice and place it in the context of earlier definitions of non-interference. We show that such an extension leads to the capturing of violations of information flow security in a concrete setting of a sequential language. Finally, we generalize non-interference for concurrent programs and illustrate its use for security analysis, particularly in the cases where information is transmitted through shared variables.
Cideron, Geoffrey, Seurin, Mathieu, Strub, Florian, Pietquin, Olivier.  2020.  HIGhER: Improving instruction following with Hindsight Generation for Experience Replay. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). :225–232.
Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. While these characterizations may foster instructing, conditioning or structuring interactive agent behavior, it remains an open-problem to correctly relate language understanding and reinforcement learning in even simple instruction following scenarios. This joint learning problem is alleviated through expert demonstrations, auxiliary losses, or neural inductive biases. In this paper, we propose an orthogonal approach called Hindsight Generation for Experience Replay (HIGhER) that extends the Hindsight Experience Replay approach to the language-conditioned policy setting. Whenever the agent does not fulfill its instruction, HIGhER learns to output a new directive that matches the agent trajectory, and it relabels the episode with a positive reward. To do so, HIGhER learns to map a state into an instruction by using past successful trajectories, which removes the need to have external expert interventions to relabel episodes as in vanilla HER. We show the efficiency of our approach in the BabyAI environment, and demonstrate how it complements other instruction following methods.
Segovia, Mariana, Rubio-Hernan, Jose, Cavalli, Ana R., Garcia-Alfaro, Joaquin.  2020.  Cyber-Resilience Evaluation of Cyber-Physical Systems. 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). :1—8.
Cyber-Physical Systems (CPS) use computational resources to control physical processes and provide critical services. For this reason, an attack in these systems may have dangerous consequences in the physical world. Hence, cyber- resilience is a fundamental property to ensure the safety of the people, the environment and the controlled physical processes. In this paper, we present metrics to quantify the cyber-resilience level based on the design, structure, stability, and performance under the attack of a given CPS. The metrics provide reference points to evaluate whether the system is better prepared or not to face the adversaries. This way, it is possible to quantify the ability to recover from an adversary using its mathematical model based on actuators saturation. Finally, we validate our approach using a numeric simulation on the Tennessee Eastman control challenge problem.
Bosio, Alberto, Canal, Ramon, Di Carlo, Stefano, Gizopoulos, Dimitris, Savino, Alessandro.  2020.  Cross-Layer Soft-Error Resilience Analysis of Computing Systems. 2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S). :79—79.
In a world with computation at the epicenter of every activity, computing systems must be highly resilient to errors even if miniaturization makes the underlying hardware unreliable. Techniques able to guarantee high reliability are associated to high costs. Early resilience analysis has the potential to support informed design decisions to maximize system-level reliability while minimizing the associated costs. This tutorial focuses on early cross-layer (hardware and software) resilience analysis considering the full computing continuum (from IoT/CPS to HPC applications) with emphasis on soft errors.
Zanin, M., Menasalvas, E., González, A. Rodriguez, Smrz, P..  2020.  An Analytics Toolbox for Cyber-Physical Systems Data Analysis: Requirements and Challenges. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :271–276.
The fast improvement in telecommunication technologies that has characterised the last decade is enabling a revolution centred on Cyber-Physical Systems (CPSs). Elements inside cities, from vehicles to cars, can now be connected and share data, describing both our environment and our behaviours. These data can also be used in an active way, by becoming the tenet of innovative services and products, i.e. of Cyber-Physical Products (CPPs). Still, having data is not tantamount to having knowledge, and an important overlooked topic is how should them be analysed. In this contribution we tackle the issue of the development of an analytics toolbox for processing CPS data. Specifically, we review and quantify the main requirements that should be fulfilled, both functional (e.g. flexibility or dependability) and technical (e.g. scalability, response time, etc.). We further propose an initial set of analysis that should in it be included. We finally review some challenges and open issues, including how security and privacy could be tackled by emerging new technologies.
Tian, Nianfeng, Guo, Qinglai, Sun, Hongbin, Huang, Jianye.  2020.  A Synchronous Iterative Method of Power Flow in Inter-Connected Power Grids Considering Privacy Preservation: A CPS Perspective. 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). :782–787.
The increasing development of smart grid facilitates that modern power grids inter-connect with each other and form a large power system, making it possible and advantageous to conduct coordinated power flow among several grids. The communication burden and privacy issue are the prominent challenges in the application of synchronous iteration power flow method. In this paper, a synchronous iterative method of power flow in inter-connected power grid considering privacy preservation is proposed. By establishing the masked model of power flow for each sub-grid, the synchronous iteration is conducted by gathering the masked model of sub-grids in the coordination center and solving the masked correction equation in a concentration manner at each step. Generally, the proposed method can concentrate the major calculation of power flow on the coordination center, reduce the communication burden and guarantee the privacy preservation of sub-grids. A case study on IEEE 118-bus test system demonstrate the feasibility and effectiveness of the proposed methodology.
Satılmış, Hami, Akleylek, Sedat.  2020.  Efficient Implementation of HashSieve Algorithm for Lattice-Based Cryptography. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :75—79.
The security of lattice-based cryptosystems that are secure for the post-quantum period is based on the difficulty of the shortest vector problem (SVP) and the closest vector problem (CVP). In the literature, many sieving algorithms are proposed to solve these hard problems. In this paper, efficient implementation of HashSieve sieving algorithm is discussed. A modular software library to have an efficient implementation of HashSieve algorithm is developed. Modular software library is used as an infrastructure in order for the HashSieve efficient implementation to be better than the sample in the literature (Laarhoven's standard HashSieve implementation). According to the experimental results, it is observed that HashSieve efficient implementation has a better running time than the example in the literature. It is concluded that both implementations are close to each other in terms of the memory space used.