• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.323-325
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• 2016

Computing loud arrival times were, important data Clouding and, without being limited to the device, may process the information. Recently, work environment and improved access to Cloud and Mobile, this decision has been made to take effect immediately. However, when such important decisions of the government, the security is required. In this paper, we study the network access and control in IoT, Cloud, Bigdata, Smartwork System applied to Mobile. Study the authentication, authorization, and security for each security level Level of Service to connect to the DB information. Research of this paper will be used as the basis for the information processing and decision-making system design and construction of public institutions and agencies as important information for the protection Smartwork System.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.150-164
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• 2013

Currently it is being gradually focused on the mobile application's processing performance implemented by Javascript and HTML (Hyper Text Markup Language) due to the dissemination of mobile web application supply based on the WAC (Wholesale Application Community). If the application software has a simple functional processing structure, then the problem is benign, however, the load of a browser is getting heavier as the amount of Javascript processing is being increased. There is a limitation on the processing time and capacity of the Javascript in the ordinary mobile browsers which are on the market now. In order to solve those problems, the Web Worker that is not supported from the existing Javascript technology is now provided by the HTML 5 to implement the multi thread. The Web Worker provides a mechanism that process a part from the single thread through a separate one. However, it can not guarantee the computing ability as a native application on the mobile and is not enough as a solution for improving the fundamental processing speed. The Cyclostorm overcomes the limitation of resources as a mobile client and guarantees the performance as a native application by providing high computing service and ascripting the Javascript process on the mobile to the computer server on the cloud. From the performance evaluation experiment, the Cyclostorm shows a maximally 6 times faster computing speed than in the existing mobile browser's Javascript and 3 to 6 times faster than in Web Worker of the HTML 5. In addition, the usage of memory is measured less than the existing method since the server's memory has been used. In this paper, the Cyclostorm is introduced as one of the mobile cloud computing services to conquer the limitation of the WAC based mobile browsers and to improve the existing web application's performances.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.1-9
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• 2017

Innovations from current researches on cloud computing such as applying bio-inspired computing techniques have brought new level solutions in offloading mechanisms. With the growing trend of mobile devices, mobile cloud computing can also benefit from applying bio-inspired techniques. Energy-efficient offloading mechanisms on mobile cloud systems are needed to reduce the total energy consumption but previous works did not consider energy consumption in the decision-making of task distribution. This paper proposes the Particle Swarm Optimization (PSO) as an offloading strategy of cloudlet to data centers where each task is represented as a particle during the process. The collected tasks are classified using K-means clustering on the cloudlet before applying PSO in order to minimize the number of particles and to locate the best data center for a specific task, instead of considering all tasks during the PSO process. Simulation results show that the proposed PSO excels in choosing data centers with respect to energy consumption, while it has accumulated a little more processing time compared to the other approaches.

• Journal of Computing Science and Engineering
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• v.9 no.3
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• pp.155-162
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• 2015

The last decade has seen a rapid growth in the use of mobile devices all over the world. With an increasing use of mobile devices, mobile applications are becoming more diverse and complex, demanding more computational resources. However, mobile devices are typically resource-limited (i.e., a slower-speed CPU, a smaller memory) due to a variety of reasons. Mobile users will be capable of running applications with heavy computation if they can offload some of their computations to other places, such as a desktop or server machines. However, mobile users are typically subject to dynamically changing network environments, particularly, due to user mobility. This makes it hard to choose good offloading decisions in mobile environments. In general, users' mobility can provide some hints for upcoming changes to network environments. Motivated by this, we propose a mobility model of each individual user taking advantage of the regularity of his/her mobility pattern, and develop an offloading decision-making technique based on the mobility model. We evaluate our technique through trace-based simulation with real log data traces from 14 Android users. Our evaluation results show that the proposed technique can help boost the performance of mobile devices in terms of response time and energy consumption, when users are highly mobile.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.122-125
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• 2014

본 논문에서는 실시간 모바일 클라우드 컴퓨팅(mobile cloud computing)을 위한 맵리듀스(Map Reduce) 응용 처리 기법으로써 데이터 전송 경로 관리, 노드(nod) 간 다른 처리 속도로 인한 문제점 개선을 통한 성능 향상 기법들과 맵리듀스 작업의 효과적인 반복적 및 스트리밍(streaming)실행 기법들을 분석한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3648-3663
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• 2017

With the development of cloud computing and widespread availability of mobile devices, outsourcing computation has gotten more and more attention in cloud computing services. The computation of bilinear pairing is the most expensive operation in pair-based cryptographic schemes. Currently, most of the algorithms for outsourcing bilinear pairing have small checkability or the outsourcers need to operate expensive computations. In this paper, we propose an efficient algorithm for outsourcing bilinear pairing with two servers, where the outsourcers can detect the errors with a probability of 1 if the cloud servers are dishonest, and the outsourcers are not involved in any complex computations. Finally, the performance evaluation demonstrates that the proposed algorithm is most efficient in all of fully verifiable outsourcing algorithms for bilinear pairing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.998-1019
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• 2024

Mobile cloud computing is a very attractive service paradigm that outsources users' data computing and storage from mobile devices to cloud data centers. To protect data privacy, users often encrypt their data to ensure data sharing securely before data outsourcing. However, the bilinear and power operations involved in the encryption and decryption computation make it impossible for mobile devices with weak computational power and network transmission capability to correctly obtain decryption results. To this end, this paper proposes an outsourcing decryption algorithm of verifiable transformed ciphertext. First, the algorithm uses the key blinding technique to divide the user's private key into two parts, i.e., the authorization key and the decryption secret key. Then, the cloud data center performs the outsourcing decryption operation of the encrypted data to achieve partial decryption of the encrypted data after obtaining the authorization key and the user's outsourced decryption request. The verifiable random function is used to prevent the semi-trusted cloud data center from not performing the outsourcing decryption operation as required so that the verifiability of the outsourcing decryption is satisfied. Finally, the algorithm uses the authorization period to control the final decryption of the authorized user. Theoretical and experimental analyses show that the proposed algorithm reduces the computational overhead of ciphertext decryption while ensuring the verifiability of outsourcing decryption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6169-6187
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• 2017

With the fast growth of mobile services, Mobile Cloud Computing(MCC) has gained a great deal of attention from researchers in the academic and industrial field. User authentication and privacy are significant issues in MCC environment. Recently, Tsai and Lo proposed a privacy-aware authentication scheme for distributed MCC services, which claimed to support mutual authentication and user anonymity. However, Irshad et.al. pointed out this scheme cannot achieve desired security goals and improved it. Unfortunately, this paper shall show that security features of Irshad et.al.'s scheme are achieved at the price of multiple time-consuming operations, such as three bilinear pairing operations, one map-to-point hash function operation, etc. Besides, it still suffers from two minor design flaws, including incapability of achieving three-factor security and no user revocation and re-registration. To address these issues, an enhanced and provably secure authentication scheme for distributed MCC services will be designed in this work. The proposed scheme can meet all desirable security requirements and is able to resist against various kinds of attacks. Moreover, compared with previously proposed schemes, the proposed scheme provides more security features while achieving lower computation and communication costs.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.397-398
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• 2012

본 논문에서는 클라우드 컴퓨팅(Cloud Computing) 기반 모바일 오피스 환경에서의 보안 위협에 대한 대응방안에 대해 소개한다. 클라우드 컴퓨팅 기반 모바일 오피스 환경은 회사 밖에서도 회사 내부망에 접속하여 회사 업무뿐만 아니라 문서작업과 같이 프로그램이 필요한 업무를 스마트폰에 있는 접속클라이언트를 이용하여 이용할 수 있도록 한 클라우드 컴퓨팅과 모바일 오피스의 융합 서비스이다. 본 논문에서는 보안 위협에 대한 대응 방안을 제안함으로써 안전한 클라우드 컴퓨팅 기반 모바일 오피스 환경을 제공할 수 있도록 한다.