• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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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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• v.10 no.10
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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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• v.12 no.7
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.413-426
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• 2017

In this paper, we propose an adder that can be applied to data encrypted with a fully homomorphic encryption scheme and an addition method with improved performance that can be applied when adding multiple data. The proposed arithmetic adder is based on the Kogge-Stone Adder method with the optimal circuit level among the existing hardware-based arithmetic adders and suitable to apply the cryptographic SIMD (Single Instruction for Multiple Data) function on encrypted data. The proposed multiple addition method does not add a large number of data by repeatedly using Kogge-Stone Adder which guarantees perfect addition result. Instead, when three or more numbers are to be added, three numbers are added to C (Carry-out) and S (Sum) using the full-adder circuit implementation. Adding with Kogge-Stone Adder is only when two numbers are finally left to be added. The performance of the proposed method improves dramatically as the number of data increases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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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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• v.39 no.1
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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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.861-868
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• 2023

One of the key technologies in providing data analysis in the deep learning while maintaining security is fully homomorphic encryption. Due to constraints in operations on fully homomorphically encrypted data, non-arithmetic functions used in deep learning must be approximated by polynomials. Until now, the degrees of approximation polynomials with composite minimax polynomials have been uniformly set across layers, which poses challenges for effective network designs on fully homomorphic encryption. This study theoretically proves that setting different degrees of approximation polynomials constructed by composite minimax polynomial in each layer does not pose any issues in the inference on convolutional neural networks.