Faster Fully Homomorphic Encryption: Bootstrapping in Less Than 0.1 Seconds

Chillotti, Ilaria; Gama, Nicolas; Georgieva, Mariya; Izabachène, Malika

doi:10.1007/978-3-662-53887-6_1

Cited by 473 publications

(354 citation statements)

References 20 publications

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“…Another limitation to the employment of HE schemes is their computational cost, and there are limitations on the number of multiplications that can be performed between ciphertexts. Nevertheless, some recently proposed schemes [32,33] show promise regarding the efficiency of HE.…”

Section: Boxmentioning

confidence: 99%

Privacy-Preserving Biometric Authentication: Challenges and Directions

Pagnin

Mitrokotsa

2017

Security and Communication Networks

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An emerging direction for authenticating people is the adoption of biometric authentication systems. Biometric credentials are becoming increasingly popular as a means of authenticating people due to the wide range of advantages that they provide with respect to classical authentication methods (e.g., password-based authentication). The most characteristic feature of this authentication method is the naturally strong bond between a user and her biometric credentials. This very same advantageous property, however, raises serious security and privacy concerns in case the biometric trait gets compromised. In this article, we present the most challenging issues that need to be taken into consideration when designing secure and privacypreserving biometric authentication protocols. More precisely, we describe the main threats against privacy-preserving biometric authentication systems and give directions on possible countermeasures in order to design secure and privacy-preserving biometric authentication protocols.

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Section: Boxmentioning

confidence: 99%

Privacy-Preserving Biometric Authentication: Challenges and Directions

Pagnin

Mitrokotsa

2017

Security and Communication Networks

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“…For smaller N data , the parameters can be smaller. The bit security is estimated via existing attacks on LWE in [4], [8], [16], [17]. The plaintext length Since often l n in the ciphertext space Z n+l q , our system's costs for communication and computation will mainly depend on l (and hence data dimension d) when global modulus q and precision prec are fixed.…”

Section: Our System Costs and Comparisonsmentioning

confidence: 99%

Input and Output Privacy-Preserving Linear Regression

Aono

Hayashi

Phong

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYWe build a privacy-preserving system of linear regression protecting both input data secrecy and output privacy. Our system achieves those goals simultaneously via a novel combination of homomorphic encryption and differential privacy dedicated to linear regression and its variants (ridge, LASSO). Our system is proved scalable over cloud servers, and its efficiency is extensively checked by careful experiments.

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“…The LWE-based FHEs [5,[9][10][11][12][13] enjoy higher efficiency and stronger security compared to the previous schemes [2][3][4]7] (following a similar framework to Gentry's work), due to the simple algebraic structure of the well-studied LWE [14] and classical (quantum) reduction from some apparently intractable lattice problems (e.g., GapSVP) to LWE [14,15]. The first LWE-based FHE was proposed by Brakerski and Vaikuntanathan [5] (henceforth, BV11b), who used a novel relinearization technique to construct a "somewhat homomorphic" encryption scheme based on LWE problem and introduced a novel dimension-modulus reduction technique, without resorting to the "squashing paradigm" used in the previous schemes [2][3][4]7].…”

Section: Introductionmentioning

confidence: 99%

“…It is important to note that, besides the fact that the scheme does not need an "evaluation" key, it has an interesting property of asymmetric noise growth because of its specific GSW style. Based on GSW13, a sequence of schemes was proposed, including bootstrapping schemes [11,17], multikey schemes [6,12,13], and some other related schemes [18,19] (these schemes mainly leverage the homomorphic operations of GSW13).…”

Section: Introductionmentioning

confidence: 99%

LWR-Based Fully Homomorphic Encryption, Revisited

Luo

Wang

et al. 2018

Security and Communication Networks

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Very recently, Costache and Smart proposed a fully homomorphic encryption (FHE) scheme based on the Learning with Rounding (LWR) problem, which removes the noise (typically, Gaussian noise) sampling needed in the previous lattices-based FHEs. But their scheme did not work, since the noise of homomorphic multiplication is complicated and large, which leads to failure of decryption. More specifically, they chose LWR instances as a public key and the private key therein as a secret key and then used the tensor product to implement homomorphic multiplication, which resulted in a tangly modulus problem. Recall that there are two moduli in the LWR instances, and then the moduli will tangle together due to the tensor product. Inspired by their work, we built the first workable LWR-based FHE scheme eliminating the tangly modulus problem by cleverly adopting the celebrated approximate eigenvector method proposed by Gentry et al. at Crypto 2013. Roughly speaking, we use a specific matrix multiplication to perform the homomorphic multiplication, hence no tangly modulus problem. Furthermore, we also extend the LWR-based FHE scheme to the multikey setting using the tricks used to construct LWE-based multikey FHE by Mukherjee and Wichs at Eurocrypt 2016. Our LWR-based multikey FHE construction provides an alternative to the existing multikey FHEs and can also be applied to multiparty computation with higher efficiency.

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Faster Fully Homomorphic Encryption: Bootstrapping in Less Than 0.1 Seconds

Cited by 473 publications

References 20 publications

Privacy-Preserving Biometric Authentication: Challenges and Directions

Privacy-Preserving Biometric Authentication: Challenges and Directions

Input and Output Privacy-Preserving Linear Regression

LWR-Based Fully Homomorphic Encryption, Revisited

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