• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.5153-5170
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• 2016

The benefit of the scalability and flexibility inherent in cloud computing motivates clients to upload data and computation to public cloud servers. Because data is placed on public clouds, which are very likely to reside outside of the trusted domain of clients, this strategy introduces concerns regarding the security of sensitive client data. Thus, to provide sufficient security for the data stored in the cloud, it is essential to encrypt sensitive data before the data are uploaded onto cloud servers. Although data encryption is considered the most effective solution for protecting sensitive data from unauthorized users, it imposes a significant amount of overhead during the query processing phase, due to the limitations of directly executing operations against encrypted data. Recently, substantial research work that addresses the execution of SQL queries against encrypted data has been conducted. However, there has been little research on top-k join query processing over encrypted data within the cloud computing environments. In this paper, we develop an efficient algorithm that processes a top-k join query against encrypted cloud data. The proposed top-k join processing algorithm is, at an early phase, able to prune unpromising data sets which are guaranteed not to produce top-k highest scores. The experiment results show that the proposed approach provides significant performance gains over the naive solution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.5171-5189
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• 2016

With the popularity of cloud computing, many data owners outsource their graph data to the cloud for cost savings. The cloud server is not fully trusted and always wants to learn the owners' contents. To protect the information hiding, the graph data have to be encrypted before outsourcing to the cloud. The adjacent vertex search is a very common operation, many other operations can be built based on the adjacent vertex search. A boolean adjacent vertex search is an important basic operation, a query user can get the boolean search results. Due to the graph data being encrypted on the cloud server, a boolean adjacent vertex search is a quite difficult task. In this paper, we propose a solution to perform the boolean adjacent vertex search over encrypted graph data in cloud computing (BASG), which maintains the query tokens and search results privacy. We use the Gram-Schmidt algorithm and achieve the boolean expression search in our paper. We formally analyze the security of our scheme, and the query user can handily get the boolean search results by this scheme. The experiment results with a real graph data set demonstrate the efficiency of our scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3375-3400
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• 2018

With the advent of database-as-a-service (DAAS) and cloud computing, more and more data owners are motivated to outsource their data to cloud database in consideration of convenience and cost. However, it has become a challenging work to provide security to database as service model in cloud computing, because adversaries may try to gain access to sensitive data, and curious or malicious administrators may capture and leak data. In order to realize privacy preservation, sensitive data should be encrypted before outsourcing. In this paper, we present a secure and practical system over encrypted cloud data, called QSDB (queryable and secure database), which simultaneously supports SQL query operations. The proposed system can store and process the floating point numbers without compromising the security of data. To balance tradeoff between data privacy protection and query processing efficiency, QSDB utilizes three different encryption models to encrypt data. Our strategy is to process as much queries as possible at the cloud server. Encryption of queries and decryption of encrypted queries results are performed at client. Experiments on the real-world data sets were conducted to demonstrate the efficiency and practicality of the proposed system.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.161-164
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• 2022

Homomorphic Encryption (HE) schemes have been recently growing as a reliable solution to preserve users' information owe to maintaining and operating the user data in the encrypted state. In addition to that, several Neural Networks models merged with HE schemes have been developed as a prospective tool for privacy-preserving machine learning. Those mentioned works demonstrated that it is possible to match the accuracy of non-encrypted models but there is always a trade-off in the computation time. In this work, we evaluate the implementation of CKKS HE scheme operations over the layers of a LeNet5 convolutional inference model, however, owing to the limitations of the evaluation environment, the scope of this work is not to develop a complete LeNet5 encrypted model. The evaluation was performed using the MNIST dataset with Microsoft SEAL (MSEAL) open-source homomorphic encryption library ported version on Python (PyFhel). The behavior of the encrypted model, the limitations faced and a small description of related and future work is also provided.

• 정보보호학회논문지
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• 제27권3호
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• pp.413-426
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• 2017

본 연구에서는 완전동형암호로 암호화된 데이터에 적용할 수 있는 가산기 및 다수개의 데이터를 가산할 때 적용할 수 있는 성능이 향상된 가산 방법을 제안한다. 제안 산술 가산기는 기존의 하드웨어 기반의 산술 가산기 중 최적 회로단계(level)를 가지는 Kogge-Stone Adder 방법을 기반으로 하며, 완전동형암호가 제공하는 암호학적 SIMD(Single Instruction for Multiple Data) 기법을 적용하기에 적합하게 설계되었다. 제안한 다수 가산 방법은 완벽한 가산 결과를 보장하는 Kogge-Stone Adder를 반복적으로 사용하여 다수개의 데이터를 가산하지 않고, 3개 이상의 수를 더해야 할 경우, Full-Adder를 이용하여 3개의 수를 최종 C(Carry-out)과 논리합의 결과인 S(Sum) 의 두 개로 줄인다. 이러한 과정을 반복하여 최종적으로 두 개의 수를 더할 경우에만 Kogge-Stone Adder를 사용하여 가산하는 방법이다. 제안 방법은 더하고자 하는 데이터의 개수가 많아질수록 성능이 비약적으로 향상되었고, 이를 실험을 통해 검증한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권1호
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• pp.281-298
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• 2020

Fog computing has become a popular concept in the application of internet of things (IoT). With the superiority in better service providing, the edge cloud has become an attractive solution to IoT networks. The data outsourcing scheme of IoT devices demands privacy protection as well as computation verification since the lightweight devices not only outsource their data but also their computation. Existing solutions mainly deal with the operations over encrypted data, but cannot support the computation verification in the same time. In this paper, we propose a data outsourcing scheme based on an encrypted database system with linear computation as well as efficient query ability, and enhance the interlayer program in the original system with homomorphic message authenticators so that the system could perform computational verifying. The tools we use to construct our scheme have been proven secure and valid. With our scheme, the system could check if the cloud provides the correct service as the system asks. The experiment also shows that our scheme could be as effective as the original version, and the extra load in time is neglectable.

• ETRI Journal
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• 제39권1호
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• pp.116-123
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• 2017

Message-locked encryption (MLE) is a widespread cryptographic primitive that enables the deduplication of encrypted data stored within the cloud. Practical client-side contributions of MLE, however, are vulnerable to a poison attack, and server-side MLE schemes require large bandwidth consumption. In this paper, we propose a new client-side secure deduplication method that prevents a poison attack, reduces the amount of traffic to be transmitted over a network, and requires fewer cryptographic operations to execute the protocol. The proposed primitive was analyzed in terms of security, communication costs, and computational requirements. We also compared our proposal with existing MLE schemes.

• 정보보호학회논문지
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• 제33권6호
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• pp.861-868
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• 2023

보안을 유지하는 가운데 딥 러닝을 이용하여 데이터 분석 결과를 제공하는 서비스의 핵심적인 기술 중의 하나로 완전 동형 암호가 있다. 완전 동형 암호화된 데이터 간의 연산의 제약으로 인해 딥 러닝에 사용되는 비산술 함수를 다항식으로 근사해야 한다. 현재까지는 합성 미니맥스 다항식을 사용하여 비산술 함수를 근사한 다항식을 컨볼루션 뉴럴 네트워크에 적용했을 때 계층별로 같은 차수의 다항식만 적용하였는데, 이는 완전 동형 암호를 위한 효과적인 네트워크의 설계에 어려움을 준다. 본 연구는 합성 미니맥스 다항식으로 설계한 근사 다항식의 차수를 계층별로 서로 다르게 설정하여도 컨볼루션 뉴럴 네트워크에서 데이터의 분석에 문제가 없음을 이론적으로 증명하였다.