Bootstrapping for Approximate Homomorphic Encryption

Cheon, Jung Hee; Han, Kyoung-Sik; Kim, Andrey; Kim, Miran; Song, Yongsoo

doi:10.1007/978-3-319-78381-9_14

Cited by 205 publications

(159 citation statements)

References 36 publications

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“…plaintext bits in one slot, unlike the previous FHE methods [14,16]. Let the corresponding bit length be the word size.…”

Section: Heaan Heaan's Ciphertext Can Contain Multiplementioning

confidence: 99%

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An Efficient Encrypted Floating-Point Representation Using HEAAN and TFHE

Moon

Lee

2020

Security and Communication Networks

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As a method of privacy-preserving data analysis (PPDA), a fully homomorphic encryption (FHE) has been in the spotlight recently. Unfortunately, because many data analysis methods assume that the type of data is of real type, the FHE-based PPDA methods could not support the enough level of accuracy due to the nature of FHE that fixed-point real-number representation is supported easily. In this paper, we propose a new method to represent encrypted floating-point real numbers on top of FHE. e proposed method is designed to have analogous range and accuracy to 32-bit floating-point number in IEEE 754 representation. We propose a method to perform arithmetic operations and size comparison operations. e proposed method is designed using two different FHEs, HEAAN and TFHE. As a result, HEAAN is proven to be very efficient for arithmetic operations and TFHE is efficient in size comparison. is study is expected to contribute to practical use of FHE-based PPDA.

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“…plaintext bits in one slot, unlike the previous FHE methods [14,16]. Let the corresponding bit length be the word size.…”

Section: Heaan Heaan's Ciphertext Can Contain Multiplementioning

confidence: 99%

“…However, they require a very large computation for floating-point operations. Also, as we mentioned in the previous section, HEAAN [14,16] has some limitation on representing encrypted floatingpoint numbers. Also, there has been no approach to represent encrypted floating-point numbers on top of TFHE [9,19].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Encrypted Floating-Point Representation Using HEAAN and TFHE

Moon

Lee

2020

Security and Communication Networks

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“…基于 "错误学习" (learning with error, LWE) 的 FHE 方案和基于 NTRU (number theory research unit) 的 FHE 方案在层次型方案中最具代表性, 其中基于 LWE 的方案被认为能够抵抗量子攻击 [119] . 典型的 LWE 方案包括 BV [126] , BGV [127] 和 GSW 方案 [128] 等. NTRU 方案的典型代表是 López-Alt 等 [84] [130] 、Bos 等 [131] [134] .…”

Section: 密文噪声的控制是实现全同态的关键问题按照 Fhe 采用的噪声管理技术当前的 Fhe 方案可unclassified

Research progress on big data security technology

Chen¹,

Gao²,

Huilin³

et al. 2020

Sci. Sin.-Inf.

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Independence and controllability of big data security Chinese Science Bulletin 60, 427 (2015); Research progress in the complexity theory and algorithms of big-data computation SCIENTIA SINICA Informationis 46, 1255 (2016); Real-time intelligent big data processing: technology, platform, and applications SCIENCE CHINA Information Sciences 62, 082101 (2019); Research on theory and key technology of trusted computing platform security testing and evaluation SCIENCE CHINA Information Sciences 53, 434 (2010); Earth observation big data for climate change research

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“…Gentry [Gentry, 2009] first proposed a Fully Homomorphic Encryption (FHE) scheme, which theoretically could compute any kind of arithmetic circuit, but is computationally intractable in practice. FHE evolved into more efficient schemes preserving addition and multiplication over encrypted data, such as BGV [Brakerski et al, 2011], FV [Fan and Vercauteren, 2012] or CKKS [Cheon et al, 2018], allowing approximations of multiplicative inverse, exponential and logistic function, or discrete Fourier transformation. Similar to asymmetric encryption, a public-private key pair (pub, priv) is generated.…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Security for Distributed Deep Neural Networks: Towards Data Confidentiality & Intellectual Property Protection

Gomez¹,

Wilhelm

Andújar

et al. 2019

Proceedings of the 16th International Joint Conference on E-Business and Telecommunications

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Current developments in Enterprise Systems observe a paradigm shift, moving the needle from the backend to the edge sectors of those; by distributing data, decentralizing applications and integrating novel components seamlessly to the central systems. Distributively deployed AI capabilities will thrust this transition. Several non-functional requirements arise along with these developments, security being at the center of the discussions. Bearing those requirements in mind, hereby we propose an approach to holistically protect distributed Deep Neural Network (DNN) based/enhanced software assets, i.e. confidentiality of their input & output data streams as well as safeguarding their Intellectual Property. Making use of Fully Homomorphic Encryption (FHE), our approach enables the protection of Distributed Neural Networks, while processing encrypted data. On that respect we evaluate the feasibility of this solution on a Convolutional Neuronal Network (CNN) for image classification deployed on distributed infrastructures.

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Bootstrapping for Approximate Homomorphic Encryption

Cited by 205 publications

References 36 publications

An Efficient Encrypted Floating-Point Representation Using HEAAN and TFHE

An Efficient Encrypted Floating-Point Representation Using HEAAN and TFHE

Research progress on big data security technology

Security for Distributed Deep Neural Networks: Towards Data Confidentiality & Intellectual Property Protection

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