• Journal of Information Processing Systems
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• 제9권2호
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• pp.287-300
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• 2013

Cloud storage is provided as a service in order to keep pace with the increasing use of digital information. It can be used to store data via networks and various devices and is easy to access. Unlike existing removable storage, many users can use cloud storage because it has no storage capacity limit and does not require a storage medium. Cloud storage reliability has become a topic of importance, as many users employ it for saving great volumes of data. For protection against unethical administrators and attackers, a variety of cryptography systems, such as searchable encryption and proxy re-encryption, are being applied to cloud storage systems. However, the existing searchable encryption technology is inconvenient to use in a cloud storage environment where users upload their data. This is because this data is shared with others, as necessary, and the users with whom the data is shared change frequently. In this paper, we propose a searchable re-encryption scheme in which a user can safely share data with others by generating a searchable encryption index and then re-encrypt it.

• 대한원격탐사학회지
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• 제12권1호
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• pp.60-80
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• 1996

AVHRR(Advanced Very High Resolution Radiometer) on NOAA satellite provides data in five spectral, one in visible range, one in near infrared and three in thermal range. In this paper, application of NOAA/AVHRR data is studied for environment monitoring such as cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index, forest fire, flood, snow cover and so on. The analyses for cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index and forest fire showed reasonable agreement. But monitoring for flood and snow cover was uneasy due to the limitations such as cloud contamination, low spatial resolution. So this research had only simple purpose to identify well-defined waterbody for dynamic monitoring of flood. Based on development of these basic algorithms, we have a plan to further reseach for environment monitoring using AVHRR data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3182-3203
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• 2023

With the growing adoption of cloud-based technologies, maintaining the privacy and security of cloud data has become a pressing issue. Privacy-preserving encryption schemes are a promising approach for achieving cloud data security, but they require careful design and implementation to be effective. The integrated approach to cloud data security that we suggest in this work uses CogniGate: the orchestrated permissions protocol, index trees, blockchain key management, and unique Opacus encryption. Opacus encryption is a novel homomorphic encryption scheme that enables computation on encrypted data, making it a powerful tool for cloud data security. CogniGate Protocol enables more flexibility and control over access to cloud data by allowing for fine-grained limitations on access depending on user parameters. Index trees provide an efficient data structure for storing and retrieving encrypted data, while blockchain key management ensures the secure and decentralized storage of encryption keys. Performance evaluation focuses on key aspects, including computation cost for the data owner, computation cost for data sharers, the average time cost of index construction, query consumption for data providers, and time cost in key generation. The results highlight that the integrated approach safeguards cloud data while preserving privacy, maintaining usability, and demonstrating high performance. In addition, we explore the role of differential privacy in our integrated approach, showing how it can be used to further enhance privacy protection without compromising performance. We also discuss the key management challenges associated with our approach and propose a novel blockchain-based key management system that leverages smart contracts and consensus mechanisms to ensure the secure and decentralized storage of encryption keys.

• 한국멀티미디어학회논문지
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• 제17권10호
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• pp.1182-1188
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• 2014

Recently, the research on database outsourcing has been actively done with the popularity of cloud computing. However, because users' data may contain sensitive personal information, such as health, financial and location information, the data encryption methods have attracted much interest. Existing data encryption schemes process a query without decrypting the encrypted databases in order to support user privacy protection. On the other hand, to efficiently handle the large amount of data in cloud computing, it is necessary to study the distributed index structure. However, existing index structure and query processing algorithms have a limitation that they only consider single-column query processing. In this paper, we propose a grid-based multi column indexing scheme and an encrypted query processing algorithm. In order to support multi-column query processing, the multi-dimensional index keys are generated by using a space decomposition method, i.e. grid index. To support encrypted query processing over encrypted data, we adopt the Hilbert curve when generating a index key. Finally, we prove that the proposed scheme is more efficient than existing scheme for processing the exact and range query.

• 한국멀티미디어학회논문지
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• 제18권8호
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• pp.915-924
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• 2015

With growing popularity of cloud computing services, users can more easily manage massive amount of data by outsourcing them to the cloud, or more efficiently analyse large amount of data by leveraging IT infrastructure provided by the cloud. This, however, brings the security concerns of sensitive data. To provide data security, it is essential to encrypt sensitive data before uploading it to cloud computing services. Although data encryption helps provide data security, it negatively affects the performance of massive data analytics because it forbids the use of index and mathematical operation on encrypted data. Thus, in this paper, we propose a novel algorithm which enables to efficiently process a large amount of encrypted data. In particular, we propose a novel top-k processing algorithm on the massive amount of encrypted data in the cloud computing environments, and verify the performance of the proposed approach with real data experiments.

• 융합보안논문지
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• 제13권4호
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• pp.93-100
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• 2013

2012년 시스코가 발간한 '시스코 글로벌 클라우드 인덱스 2011-2016'에 따르면 전 세계 데이터 센터 트래픽은 2016년까지 4배 가량 증가하고, 클라우드 트래픽은 6배 가량 증가할 것이라고 전망했다. 이처럼 급증하는 데이터 센터의 트래픽 대부분은 데이터 센터 및 클라우드 컴퓨팅 워크로드로 인해 발생된다. 국방부는 지난 2010년, '2012 정보화사업계획'의 일환으로 2014년까지 클라우드 컴퓨팅 기술이 포함된 국방통합정보관리소를 구축하기로 결정하였고, 현재 추진 중에 있다. 국방통합정보관리소(메가 센터) 구축 시 반드시 고려해야 할 요소 중 하나가 클라우드 컴퓨팅 트래픽이다. 국방 클라우드 컴퓨팅 시스템이 구축되고 난 이후 국방 클라우드 트래픽은 꾸준히 증가할 것이다. 본 논문에서는 국방 클라우드 컴퓨팅 시범체계를 모델로 CloudAnalyst 시뮬레이션 툴을 이용하여 클라우드 트래픽 증가에 따른 서비스 가용성을 분석하였다. 3개 시나리오를 구성하여 시뮬레이션 수행 결과, 현재 시점에서 2016년까지 예측되는 클라우드 트래픽 성장률만큼 클라우드 워크로드가 증가하여도 국방 클라우드 시범체계는 서비스 가용성을 충족한다는 결론을 도출하였다.

• 한국수자원학회논문집
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• 제56권3호
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• pp.211-224
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• 2023

본 연구에서는 합성개구레이더(Synthetic Aperture Radar, SAR) 기반의 식생을 고려하는 후방산란모델 Water Cloud Model (WCM)을 활용한 토양수분 산정 연구를 수행하였다. 금강 상류의 용담댐유역을 포함한 40 × 50 km² 영역의 Sentinel-1 SAR 및 Sentinel-2 MSI (Multi-Spectral Instrument) 영상을 수집하여 연구에 활용하였다. WCM의 식생변수로는 Sentinel-1 기반의 식생지수 RVI (Radar Vegetation Index), 탈분극비(Depolarization Rario, DR)와 Sentinel-2 기반의 NDVI (Normalized Difference Vegetation Index)를 활용하였다. WCM의 정모델링(forward modeling)은 토양수분과 후방산란계수의 특성이 유사한 3개 Group으로 나누어 수행하였다. 토양수분과 후방산란계수의 선형적인 관계가 명확할수록 Group의 모의 성능이 더 높게 나타났으며, 식생지수 별로는 NDVI, RVI, DR 순으로 정확도가 높았다. 토양수분을 모의하기 위해 모의된 후방산란계수를 반전(inversion)하였으며, 모의 성능은 정모델링 결과와 비례하였다. WCM 모의의 오류는 실측 후방산란계수 기준 약 -12dB를 기점으로 증가하는 양상을 보였다.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1987년도 춘계학술발표회 논문집
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• pp.9-13
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• 1987

K-emans alsor/thm was used to classify cloud-type that is low, mix and cumuionimbus Tnitiat ciustercenters and K parameter is given in this paper by coatse computins and Fisher’s alsorithm. Results indicate that performance index is minimized and mix cloud is well clallified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5692-5716
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• 2019

One of the best means to safeguard the confidentiality, security, and privacy of an image within the IoT-Cloud is through encryption. However, looking through encrypted data is a difficult process. Several techniques for searching encrypted data have been devised, but certain security solutions may not be used in IoT-Cloud because such solutions are not lightweight. We propose a lightweight scheme that can perform a content-based search of encrypted images, namely EEIRI. In this scheme, the images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two descriptor sets. To improve the search efficiency, we employ the k-means clustering technique to construct a searchable tree-based index. Our index construction process ensures the privacy of the stored data and search requests. When compared with more familiar techniques of searching images over plaintexts, EEIRI is considered to be more efficient, demonstrating a higher search cost of 7% and a decrease in search accuracy of 1.7%. Numerous empirical investigations are carried out in relation to real image collections so as to evidence our work.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.28-28
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• 2022

본 연구는 용담댐유역을 포함한 금강 유역 상류 지역을 대상으로 Sentinel-1 SAR (Synthetic Aperture Radar) 위성영상을 기반으로 한 토양수분 산정을 목적으로 하였다. Sentinel-1 영상은 2019년에 대해 12일 간격으로 수집하였고, 영상의 전처리는 SNAP (SentiNel Application Platform)을 활용하여 기하 보정, 방사 보정 및 Speckle 보정을 수행하여 VH (Vertical transmit-Horizontal receive) 및 VV (Vertical transmit-Vertical receive) 편파 후방산란계수로 변환하였다. 토양수분 산정에는 Water Cloud Model (WCM)이 활용되었으며, 모형의 식생 서술자(Vegetation descriptor)는 RVI (Radar Vegetation Index)와 NDVI (Normalized Difference Vegetation Index)를 활용하였다. RVI는 Sentinel-1 영상의 VH 및 VV 편파자료를 이용해 산정하였으며, NDVI는 동기간에 대해 10일 간격으로 수집된 Sentinel-2 MSI (MultiSpectral Instrument) 위성영상을 활용하여 산정하였다. WCM의 검정 및 보정은 한국수자원공사에서 제공하는 10 cm 깊이의 TDR (Time Domain Reflectometry) 센서에서 실측된 6개 지점의 토양수분 자료를 수집하여 수행하였으며, 매개변수의 최적화는 비선형 최소제곱(Non-linear least square) 및 PSO (Particle Swarm Optimization) 알고리즘을 활용하였다. WCM을 통해 산정된 토양수분은 피어슨 상관계수(Pearson's correlation coefficient)와 평균제곱근오차(Root mean square error)를 활용하여 검증을 수행할 예정이다.