Earticle

As the works on quantum computing have seen substantial development in recent years, we are on the verge of seeing the fruitful result of the preliminary, early version of quantum computers. Nevertheless, in order to achieve full-fledged, large-scale quantum computers, two aspects still lacking in the existing quantum computers, i.e., scalability and reliability, will need to be more carefully considered and investigated. In this survey, we present a review of existing literature that aims to alleviate the scalability and reliability issues in quantum computers. In particular, we discuss how existing research leads to two main solutions: for advancing scalability, a distributed quantum computing (DQC) paradigm will be the primary direction instead of the general centralized quantum computing (CQC), whereas for attaining reliability, a fault-tolerant quantum computation (FTQC) approach would need to be leveraged instead of the existing noisy intermediate-scale quantum computers (NISQ). Combining both solutions, we highlight the essentiality of fault-tolerant distributed quantum computation (FT-DQC) and present related progress in the field. Furthermore, from the papers that we have gathered, we provide the taxonomy of the works to give a clearer landscape of the field and discuss the key issues in realizing FT-DQC.

Data mining is the process of extracting information from data warehousing applications. Data outsourcing is the major task in present days, for accessing services and other features of the database processing. But sometimes this process may achieve to split among various parties with recommended data items in analyzing of the data. Data security is one of the key processes in outsourcing data to various outside users. Traditionally Fast Distribution Mining algorithm was proposed for securing distributed data. This paper addresses a problem by secure association rules over partitioned data in both horizontal and vertical representation. A secure frequency developed algorithm is used for doing above process efficiently in partitioned data, which includes services of the data in outsourcing process. Frequent item sets are used to access services in outsourcing data in recent application development data mining. Our proposed work maintains efficient security over vertical and horizontal view of representation in secure mining applications. The result shows that algorithm timing is desirable for big size data for security considerations using association rule mining operations in real time application development.

With the development of wireless communication technologies and popularity of smart phones, many people are using location-based services (LBSs). However, since users continuously request LBS queries to LBS server by sending their exact location data, their privacy information is in danger. Therefore, cloaking area creation schemes have been proposed to protect the user’s location privacy. However, because the existing techniques generate a cloaking area in a centralized manner, an anonymizer suffers from performance degradation and security problem. Therefore, a cloaking area creation scheme based on distributed system is required for the safe and comfortable use of mobile users. In this paper, we propose a grid-based cloaking area creation scheme for continuous LBS queries in distributed systems. The proposed scheme stores information and performs operations in a distributed manner to create a cloaking area. In addition, the proposed scheme uses aggregate nodes to prevent query issuers' bottleneck. Finally, we show from a performance analysis that our cloaking area creation scheme shows better performance than the existing centralized cloaking area creation schemes.

Substitution boxes are among the most critical components of a secure block cipher de- sign. A substitution box or, for short, SBox, is a set of Boolean functions implementing a nonlinear mapping of inputs to outputs and it is employed for mixing the input of the cipher with the encryption key so that the output of the cipher reveals no information about the encryption key. Over the years, a number of good practices have evolved that, if employed, can lead to the construction of SBoxes with good security properties that lead to the cipher’s resistance against known and envisaged cryptanalysis techniques. One such practice is the employment of particular classes of Boolean functions that possess a number of desirable properties. A drawback of such an approach is that it is frequently a computationally demanding task to check that the employed Boolean functions and the resulting SBox have the target properties. In this paper we describe a distributed algorithm that can accelerate significantly the construction of SBoxes with desirable cryptographic properties. The algorithm has been implemented on a computer cluster and it is fully parametric, with respect to the type of Boolean functions it can use as constituents of the SBox. A designer can use a class of Boolean functions with algorithmically definable properties in order to produce SBoxes of arbitrary sizes. We also present evidence for the algorithm’s efficiency by comparing it with the best sequential approach available in a series of different experimental setups.

Distributed computation follows the models of discrete structures in combinatorial forms. In higher-dimensions, the simplex structures of topological spaces as well as homology are employed to model and analyze distributed asynchronous computations. However, the monotone spaces are the general forms of topological spaces and can be effectively employed to analyze distributed computation. This paper proposes an analytical model of distributed computation in monotone spaces. It is illustrated that, the modeling of distributed computation in monotone spaces helps in determining consistent cuts under closure and convergence of computation. Furthermore, a connective mapping between the simplexes and monotone is constructed.

We propose a new distributed QoS routing scheme called pre-computation based selective probing (PCSP). The PCSP scheme is designed to provide an exact solution to the constrained optimization problem with moderate overhead, considering the practical environment where the state information available for the routing decision is not exact. It does not limit the number of probe messages, instead, employs a qualitative (or conditional) selective probing approach. It considers both the cost and QoS metrics of the least-cost and the best-QoS paths to calculate the end-to-end cost of the found feasible paths and find QoS-satisfying least-cost paths. It defines strict probing condition that excludes not only the non-feasible paths but also the non-optimal paths. It additionally pre-computes the QoS variation taking into account the impreciseness of the state information and applies two modified QoS-satisfying conditions to the selection rules. This strict probing condition and carefully designed probing approaches enable to strictly limit the set of neighbor nodes involved in the probing process, thereby reducing the message overhead without sacrificing the optimal properties. However, the PCSP scheme may suffer from high message overhead due to its conservative search process in the worst case. In order to bound such message overhead, we extend the PCSP algorithm by applying additional quantitative heuristics. Computer simulations reveal that the PCSP scheme reduces message overhead and possesses ideal success ratio with guaranteed optimal search. In addition, the quantitative extensions of the PCSP scheme turn out to bound the worst-case message overhead with slight performance degradation.

최근 대용량 데이터의 처리능력이 요구됨에 따라 클라우드 컴퓨팅 환경에서 비즈니스 플랫폼을 제공하고자 하는 기업이 증가하고 있다. 이러한 비즈니스 플랫폼 구축을 위한 대용량 분산 시스템 기술은 데이터를 대량으로 저장하고 고속, 병렬 처리를 가능하게 한다. MapReduce는 데이터를 병렬처리하여 분산계산하는 프레임워크로서 데이터마이닝, genomic computation 등에 다양하게 응용될 수 있지만, 개인정보 등 민감한 데이터를 Map/Reduce하는 과정에서 프라이버시의 침해가 발생할 가능성이 높다. 제안하는 기법은 HE(Homomorphic Encryption)를 Hadoop의 MapReduce구조에 도입하는 것이다. 그리고 2차 활용을 위한 대용량 데이터의 연산 시 암호화된 데이터의 계산을 수행함으로써 개인정보의 노출을 배제하여 end-to-end 기밀성을 향상시키는 기법이다. 본 논문에서는 MapReduce 기반의 대용량 e-비즈니스 데이터를 위한 분산계산 구조를 제안하고 Hadoop HDFS상에서 HE를 실행함으로써 계산결과에 대한 검증을 행한다.

Lately, owing to request of processing capability for a large amount data in cloud environment, the corporations for providing business platform are increased. The technology of large-scale distributed processing system for consructing business platforme enables large amount data to store and process parallelly. MapReduce is a framework for processing huge datasets. And it is largely used in data mining, genomic computation and social networks. But it has a possible occurrence of invasion of privacy against sensitive data including individual information etc. in processing MapReduce. We present MapReduce based distributed computation architecture using HE(Homomorphic Encryption) which provides privacy guarantees for distributed computations on all data. Also, we validate our results by experimenting HE on Hadoop HDFS.

Tree network consisting of communicating processors is considered. The objective is to minimize the computation time by distributing the processing load to other nodes. The effect of the order of load distribution on the processing time is addressed. An algorithm which optimally determines the order of load distribution is developed. It is shown that the order depends only on the channel capacity between nodes but not on the computing capability of each node.

A research to evaluate efficiency of design optimization was performed for aerodynamic design optimization problem in distributed computing environment. The aerodynamic analyses which take most of computational work during design optimization were divided into several jobs and allocated to associated PC clients through network. This is not a parallel process based on domain decomposition rather than a simultaneous distributed-analyses process using network-distributed computers. GBOM(gradient-based optimization method), SAO(Sequential Approximate Optimization) and RSM(Response Surface Method) were implemented to perform design optimization of transonic airfoil and to evaluate their efficiencies. One dimensional minimization followed by direction search involved in the GBOM was found an obstacle against improving efficiency of the design process in distributed computing environment. The SAO was found quite suitable for the distributed computing environment even it has a handicap of local search. The RSM is apparently the fittest for distributed computing environment, but additional trial and error works needed to enhance the reliability of the approximation model are annoying and time-consuming so that they often impair the automatic capability of design optimization and also deteriorate efficiency from the practical point of view.

The subject of this research is not only to examine effects on electrical wiring that voltage drops make in low voltage load facilities found in apartments, buildings street lightings, factories, and etc, but also to discuss the voltage drops computation program on distributed random loads to optimize their safety and economy.

문제의 병행처리를 위한 모델링 방법과 문제의 병행성을 동적 환경에서 자동으로 검출하기 위하여 병행 연산모델 두레를 설계하고 두레언어 DL로 구현하였다. 두레모델은 문제의 모델링과 연산수행을 위해 단순하고 일관된 개념의 객체 정의와 메시지 전송개념을 지원한다. 문제의 병행처리를 프로그램에 명시하지 않고도 암시적으로 지원할 수 있도록 병행성의 검출 기준을 제안하였으며, 동적 환경에서 병행객체를 생성하여 최대한의 병행성을 보장하도록 하였다. 또한 객체의 연산 진행을 효율적으로 보장해 줄 수 있도록 Waiting Variable의 개념을 제안하였다.

A concurrent computation model Doorae and its description language DL are developed to model problems of parallel and distributed systems. Doorae model has simple and uniform concepts of object and message passing for problem modeling and computation. A method for detecting parallelism implicitly. with no exact description of parallelism in program. is proposed. Furthermore, the method assures the maximum parallelism in dynamic environment by creating concurrent objects. Also a concept of Waiting Variable to insure maximum computation efficiency of objects is proposed.

전선에서 전압강하는 열 손실을 의미한다. 이 열은 절연체의 특성을 변화시켜 절연성능을 저하시키게 되고 나아가 누전, 감전, 정전 및 화재 등의 사고를 일으키는 원인이 되기도 한다. 그러므로 전압강하에 대한 최적화 설계는 전기설비의 안전성과 경제성을 보장하는 중요한 요소가 되고 있다. 이에 저압의 전기설비인 가로등, 빌딩, 지하철역사 등 공공의 안전확보가 요구되는 다중 분산부하계통에서의 전압강하가 전기배선에 미치는 영향을 분석하고, 안전성 및 경제성 확보를 위한 전압강하산정 프로그램 및 최적화 방안을 연구 제시하고자 한다.

The voltage drop in electrical circuits causes a heat accumulation of wiring. This heat could change the specific character of an insulator and deteriorate the dielectric strength so that it could affect an electric leakage, an electric ant a power failure and a fire caused by the short circuit. Therefore optimal design for voltage drop in electrical wiring became an important factor to ensure the safety and economical efficiency of electrical facilities. So in this paper, I analyzed consequences that voltage drop affects in electrical wiring in multi-distributed system used for low electrical system such as road lights, Building, subway station which needs securities for the public society. And I proposed voltage drops computation program and optimal design to ensure the safety and economical efficiency.