• Title, Summary, Keyword: homomorphic encryption

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A General Design Method of Constructing Fully Homomorphic Encryption with Ciphertext Matrix

  • Song, Xinxia;Chen, Zhigang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.5
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    • pp.2629-2650
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    • 2019
  • It is important to construct fully homomorphic encryption with ciphertext matrix that makes fully homomorphic encryption become very nature and simple. We present a general design method of constructing fully homomorphic encryption whose ciphertext is matrix. By using this design method, we can deduce a fully homomorphic encryption scheme step by step based on a basic encryption scheme. The process of deduction is similar to solving equation and the final output result is a fully homomorphic encryption scheme with ciphertext matrix. The idea of constructing ciphertext matrix is ciphertexts stack, which don't simply stack ciphertexts together but is to obtain the desired homomorphic property. We use decryption structure as tool to analyze homomorphic property and noise growth during homomorphic evaluation. By using this design method, we obtain three corresponding fully homomorphic encryption schemes. Our obtained fully homomorphic encryption schemes are more efficient. Finally, we introduce the adversary advantage and improve the previous method of estimating concert parameters of fully homomorphic encryption. We give the concert parameters of these schemes.

A Speech Homomorphic Encryption Scheme with Less Data Expansion in Cloud Computing

  • Shi, Canghong;Wang, Hongxia;Hu, Yi;Qian, Qing;Zhao, Hong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.5
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    • pp.2588-2609
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    • 2019
  • Speech homomorphic encryption has become one of the key components in secure speech storing in the public cloud computing. The major problem of speech homomorphic encryption is the huge data expansion of speech cipher-text. To address the issue, this paper presents a speech homomorphic encryption scheme with less data expansion, which is a probabilistic statistics and addition homomorphic cryptosystem. In the proposed scheme, the original digital speech with some random numbers selected is firstly grouped to form a series of speech matrix. Then, a proposed matrix encryption method is employed to encrypt that speech matrix. After that, mutual information in sample speech cipher-texts is reduced to limit the data expansion. Performance analysis and experimental results show that the proposed scheme is addition homomorphic, and it not only resists statistical analysis attacks but also eliminates some signal characteristics of original speech. In addition, comparing with Paillier homomorphic cryptosystem, the proposed scheme has less data expansion and lower computational complexity. Furthermore, the time consumption of the proposed scheme is almost the same on the smartphone and the PC. Thus, the proposed scheme is extremely suitable for secure speech storing in public cloud computing.

A Survey of Homomorphic Encryption for Outsourced Big Data Computation

  • Fun, Tan Soo;Samsudin, Azman
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.8
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    • pp.3826-3851
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    • 2016
  • With traditional data storage solutions becoming too expensive and cumbersome to support Big Data processing, enterprises are now starting to outsource their data requirements to third parties, such as cloud service providers. However, this outsourced initiative introduces a number of security and privacy concerns. In this paper, homomorphic encryption is suggested as a mechanism to protect the confidentiality and privacy of outsourced data, while at the same time allowing third parties to perform computation on encrypted data. This paper also discusses the challenges of Big Data processing protection and highlights its differences from traditional data protection. Existing works on homomorphic encryption are technically reviewed and compared in terms of their encryption scheme, homomorphism classification, algorithm design, noise management, and security assumption. Finally, this paper discusses the current implementation, challenges, and future direction towards a practical homomorphic encryption scheme for securing outsourced Big Data computation.

An Efficient Homomorphic Encryption Addition Method using Logic Circuit Arithmetic (논리 회로 연산을 이용한 효율적인 동형암호 덧셈 연산 기법)

  • Lee, Saet-Byeol;Song, Baek-Kyung;Yoon, Ji-Won
    • Journal of Security Engineering
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    • v.14 no.6
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    • pp.501-510
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    • 2017
  • As cloud services evolve, the issue of how to preserve the privacy of data stored in cloud servers is also getting attention. Homomorphic Encryption is one of the most powerful solutions for cloud security, but it is not being applied to the real cloud environment due to the inherent limitations which it has. The biggest limitation of Homomorphic Encryption is that noise is accumulated in ciphertext each time an operation between ciphertexts is performed. Also, Homomorphic Encryption supports only simple ciphertext operations such as addition and multiplication between integers are possible. Although there have been many studies to overcome its limitations, most of them can be used only under certain conditions, but they have not solved fundamental problems. In this paper, we analyze the limitations of existing Homomorphic Encryption algorithms, and construct a homomorphic addition operation that can be used universally using logic circuit operations.

Quantum Error Correction Code Scheme used for Homomorphic Encryption like Quantum Computation (동형암호적 양자계산이 가능한 양자오류정정부호 기법)

  • Sohn, Il Kwon;Lee, Jonghyun;Lee, Wonhyuk;Seok, Woojin;Heo, Jun
    • Convergence Security Journal
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    • v.19 no.3
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    • pp.61-70
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    • 2019
  • Recently, developments on quantum computers and cloud computing have been actively conducted. Quantum computers have been known to show tremendous computing power and Cloud computing has high accessibility for information and low cost. For quantum computers, quantum error correcting codes are essential. Similarly, cloud computing requires homomorphic encryption to ensure security. These two techniques, which are used for different purposes, are based on similar assumptions. Then, there have been studies to construct quantum homomorphic encryption based on quantum error correction code. Therefore, in this paper, we propose a scheme which can process the homomorphic encryption like quantum computation by modifying the QECCs. Conventional quantum homomorphic encryption schemes based on quantum error correcting codes does not have error correction capability. However, using the proposed scheme, it is possible to process the homomorphic encryption like quantum computation and correct the errors during computation and storage of quantum information unlike the homogeneous encryption scheme with quantum error correction code.

A Survey of applying Fully Homomorphic Encryption in the Cloud system (클라우드 컴퓨팅 환경에서의 개인정보보호를 위한 완전 동형 암호 적용 방안 고찰)

  • Kim, Sehwan;Yoon, Hyunsoo
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.24 no.5
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    • pp.941-949
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    • 2014
  • Demands for cloud computing service rapidly increased along with the expansion of supplying smart devices. Interest in cloud system has led to the question whether it is really safe. Due to the nature of cloud system, cloud service provider can get a user's private information and disclose it. There is a large range of opinion on this issue and recently many researchers are looking into fully homomorphic encryption as a solution for this problem. Fully homomorphic encryption can permit arbitrary computation on encrypted data. Many security threats will disappear by using fully homomorphic encryption, because fully homomorphic encryption keeps the confidentiality. In this paper, we research possible security threats in cloud computing service and study on the application method of fully homomorphic encryption for cloud computing system.

Technical Trend of Fully Homomorphic Encryption (완전동형암호 기술의 연구 동향)

  • Jeong, Myoung In
    • The Journal of the Korea Contents Association
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    • v.13 no.8
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    • pp.36-43
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    • 2013
  • Fully homomorphic encryption is a cryptography system in which coded data can be searched and statistically processed without decryption. Fully homomorphic encryption has accelerated searching speed by minimizing time spent on encryption and decryption. In addition, it is also known to prevent leakage of any data decoded for statistical reasons. Also, it is expected to protect personal information stored in the cloud computing environment which is becoming commercialized. Since the 1970s when fully homomorphic encryption was first introduced, it has been researched to develop the algorithm that satisfy effectiveness and functionality. We will take the reader through a journey of these developments and provide a glimpse of the exciting research directions that lie ahead.

Message Expansion of Homomorphic Encryption Using Product Pairing

  • Eom, Soo Kyung;Lee, Hyang-Sook;Lim, Seongan
    • ETRI Journal
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    • v.38 no.1
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    • pp.123-132
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    • 2016
  • The Boneh, Goh, and Nissim (BGN) cryptosytem is the first homomorphic encryption scheme that allows additions and multiplications of plaintexts on encrypted data. BGN-type cryptosystems permit very small plaintext sizes. The best-known approach for the expansion of a message size by t times is one that requires t implementations of an initial scheme; however, such an approach becomes impractical when t is large. In this paper, we present a method of message expansion of BGN-type homomorphic encryption using composite product pairing, which is practical for relatively large t. In addition, we prove that the indistinguishability under chosen plaintext attack security of our construction relies on the decisional Diffie-Hellman assumption for all subgroups of prime order of the underlying composite pairing group.

Query with SUM Aggregate Function on Encrypted Floating-Point Numbers in Cloud

  • Zhu, Taipeng;Zou, Xianxia;Pan, Jiuhui
    • Journal of Information Processing Systems
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    • v.13 no.3
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    • pp.573-589
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    • 2017
  • Cloud computing is an attractive solution that can provide low cost storage and powerful processing capabilities for government agencies or enterprises of small and medium size. Yet the confidentiality of information should be considered by any organization migrating to cloud, which makes the research on relational database system based on encryption schemes to preserve the integrity and confidentiality of data in cloud be an interesting subject. So far there have been various solutions for realizing SQL queries on encrypted data in cloud without decryption in advance, where generally homomorphic encryption algorithm is applied to support queries with aggregate functions or numerical computation. But the existing homomorphic encryption algorithms cannot encrypt floating-point numbers. So in this paper, we present a mechanism to enable the trusted party to encrypt the floating-points by homomorphic encryption algorithm and partial trusty server to perform summation on their ciphertexts without revealing the data itself. In the first step, we encode floating-point numbers to hide the decimal points and the positive or negative signs. Then, the codes of floating-point numbers are encrypted by homomorphic encryption algorithm and stored as sequences in cloud. Finally, we use the data structure of DoubleListTree to implement the aggregate function of SUM and later do some extra processes to accomplish the summation.

Secure Outsourced Computation of Multiple Matrix Multiplication Based on Fully Homomorphic Encryption

  • Wang, Shufang;Huang, Hai
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.11
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    • pp.5616-5630
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    • 2019
  • Fully homomorphic encryption allows a third-party to perform arbitrary computation over encrypted data and is especially suitable for secure outsourced computation. This paper investigates secure outsourced computation of multiple matrix multiplication based on fully homomorphic encryption. Our work significantly improves the latest Mishra et al.'s work. We improve Mishra et al.'s matrix encoding method by introducing a column-order matrix encoding method which requires smaller parameter. This enables us to develop a binary multiplication method for multiple matrix multiplication, which multiplies pairwise two adjacent matrices in the tree structure instead of Mishra et al.'s sequential matrix multiplication from left to right. The binary multiplication method results in a logarithmic-depth circuit, thus is much more efficient than the sequential matrix multiplication method with linear-depth circuit. Experimental results show that for the product of ten 32×32 (64×64) square matrices our method takes only several thousand seconds while Mishra et al.'s method will take about tens of thousands of years which is astonishingly impractical. In addition, we further generalize our result from square matrix to non-square matrix. Experimental results show that the binary multiplication method and the classical dynamic programming method have a similar performance for ten non-square matrices multiplication.