Leveled Certificateless Fully Homomorphic Encryption Schemes From Learning With Errors

Li, Mingxiang

doi:10.1109/access.2020.2971342

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…This special property allows third parties to perform algebraic operations on the ciphertext, without the need for decryption operations throughout the process. According to the supported operations, homomorphic encryption can be classified into fully homomorphic encryption (FHE) [27,28], leveled fully homomorphic encryption (LFHE) [29], additional homomorphic encryption (AHE) [30] and multiplicative homomorphic encryption (MHE) [31].…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

Yin,

Peng,

Tan

et al. 2024

Computer Modeling in Engineering &Amp; Sciences

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In the assessment of car insurance claims, the claim rate for car insurance presents a highly skewed probability distribution, which is typically modeled using Tweedie distribution. The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset, when the data is provided by multiple parties, training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge. To address this issue, this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos. The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data. After determining which entities are shared, the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters. The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model. Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data from both parties without exchanging data. The assessment results of the scheme approach those of the Tweedie regression model learned from centralized data, and outperform the Tweedie regression model learned independently by a single party.

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Section: Homomorphic Encryptionmentioning

confidence: 99%

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

Yin,

Peng,

Tan

et al. 2024

Computer Modeling in Engineering &Amp; Sciences

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“…α turns out to be the primitive element of the field. This primitive element, generates a cyclic group of 15 elements, which is a field [20].…”

Section: Examplementioning

confidence: 99%

“…Therefore, over the last few years , a variety of studies have been performed to examine the efficiency and safety of FHE schemes on more criteria and well-understood security assumptions. From a security perspective, a series of papers [15]- [20] led to a graded FHE scheme based on the same complexity as the underlying non-homomorphic lattice-based encryption. According to Brakerski [17], in the Chosen Plaintext Attack (CPA) security model such as Oc-toM, linearly decryptable FHE schemes can not be secured.…”

Section: Introductionmentioning

confidence: 99%

Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra

Mustafa

Azar

et al. 2020

IEEE Access

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Among several approaches to privacy-preserving cryptographic schemes, we have concentrated on noise-free homomorphic encryption. It is a symmetric key encryption that supports homomorphic operations on encrypted data. We present a fully homomorphic encryption (FHE) scheme based on sedenion algebra over finite Z n rings. The innovation of the scheme is the compression of a 16-dimensional vector for the application of Frobenius automorphism. For sedenion, we have p 16 different possibilities that create a significant bijective mapping over the chosen 16-dimensional vector that adds permutation to our scheme. The security of this scheme is based on the assumption of the hardness of solving a multivariate quadratic equation system over finite Z n rings. The scheme results in 256n multivariate polynomial equations with 256 + 16n unknown variables for n messages. For this reason, the proposed scheme serves as a security basis for potentially post-quantum cryptosystems. Moreover, after sedenion, no newly constructed algebra loses its properties. This scheme would therefore apply as a whole to the following algebras, such as 32dimensional trigintadunion.

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“…Other extensions of levelled FHE constructions include identity-based levelled FHE schemes [47][48][49], multi-identity and multi-key levelled FHE schemes [50,51], levelled certificateless FHE schemes [52,53], leakage resilient levelled FHE schemes [54] etc.…”

Section: Introductionmentioning

confidence: 99%

Towards an efficient LWE‐based fully homomorphic encryption scheme

Dowerah

Krishnaswamy

2022

IET Information Security

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The security of most early fully homomorphic encryption schemes was based on the hardness of the Learning with Errors (LWE) problem. These schemes were inefficient in terms of per gate computations and public‐key size. More efficient schemes were later developed based on the hardness of the Ring‐LWE (RLWE) problem. While the hardness of the LWE problem is based on the hardness of the approximate shortest vector problem (GapSVPγ) over regular lattices, the hardness of the RLWE problem is based on the hardness of the approximate shortest vector problem over ideal lattices. As of now, it has not been proved that the (GapSVPγ) problem over ideal lattices is as difficult as the corresponding problem over regular lattices. In this work, the authors propose a multi‐bit levelled fully homomorphic encryption scheme using multivariate polynomial evaluations whose security depends on the hardness of the LWE problem. In terms of per gate computation cost, this scheme is more efficient than existing LWE‐based schemes. Further, for an appropriate choice of parameters, the per computation cost for homomorphic multiplication can be made asymptotically comparable to RLWE‐based schemes in a parallel computing environment. For homomorphic multiplication, the scheme uses a polynomial‐based technique that does not require relinearization (and key switching).

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Leveled Certificateless Fully Homomorphic Encryption Schemes From Learning With Errors

Cited by 9 publications

References 48 publications

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra

Towards an efficient LWE‐based fully homomorphic encryption scheme

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