Homomorphic SIM$$^2$$D Operations: Single Instruction Much More Data

Castryck, Wouter; Iliashenko, Ilia; Vercauteren, Fréderik

doi:10.1007/978-3-319-78381-9_13

Cited by 9 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When we look purely at the performance of the FV homomorphic encryption scheme, we might consider a residue number system (RNS) variant of the FV scheme as described in [ 16 ] to further improve the running time of our implementation. One could also consider single instruction multiple data (SIMD) techniques as suggested in [ 17 ] or look further into a dynamic rescaling procedure for FV as mentioned in [ 6 ]. These techniques will presumably further decrease the running time of our implementation, which would render our solution even more valuable.…”

Section: Discussionmentioning

confidence: 99%

Privacy-preserving logistic regression training

Bonte

Vercauteren

2018

BMC Med Genomics

Self Cite

View full text Add to dashboard Cite

BackgroundLogistic regression is a popular technique used in machine learning to construct classification models. Since the construction of such models is based on computing with large datasets, it is an appealing idea to outsource this computation to a cloud service. The privacy-sensitive nature of the input data requires appropriate privacy preserving measures before outsourcing it. Homomorphic encryption enables one to compute on encrypted data directly, without decryption and can be used to mitigate the privacy concerns raised by using a cloud service.MethodsIn this paper, we propose an algorithm (and its implementation) to train a logistic regression model on a homomorphically encrypted dataset. The core of our algorithm consists of a new iterative method that can be seen as a simplified form of the fixed Hessian method, but with a much lower multiplicative complexity.ResultsWe test the new method on two interesting real life applications: the first application is in medicine and constructs a model to predict the probability for a patient to have cancer, given genomic data as input; the second application is in finance and the model predicts the probability of a credit card transaction to be fraudulent. The method produces accurate results for both applications, comparable to running standard algorithms on plaintext data.ConclusionsThis article introduces a new simple iterative algorithm to train a logistic regression model that is tailored to be applied on a homomorphically encrypted dataset. This algorithm can be used as a privacy-preserving technique to build a binary classification model and can be applied in a wide range of problems that can be modelled with logistic regression. Our implementation results show that our method can handle the large datasets used in logistic regression training.

show abstract

Section: Discussionmentioning

confidence: 99%

Privacy-preserving logistic regression training

Bonte

Vercauteren

2018

BMC Med Genomics

Self Cite

View full text Add to dashboard Cite

show abstract

“…After Gentry's breakthrough work, many of its implementations and improved schemes are proposed. They are based on different cryptographic assumptions: approximate greatest common divisors [6][7][8][9][10], standard learning with errors (LWE) [11][12][13][14][15][16], and ring-LWE (RLWE) [17][18][19][20][21].…”

Section: Fig 1 Private Cloud Computing Based On Fhementioning

confidence: 99%

“…(see e.g. [8,10,20,38]). This makes it be one of the most important and powerful tools in FHE schemes.…”

Section: Our Basic Pir Protocol From Irgswmentioning

confidence: 99%

One Improved RLWE-based FHE and Fast Private Information Retrieval

Song¹,

Hu²,

Zhao³

2019

KSII TIIS

View full text Add to dashboard Cite

With the rapid development of cloud computing, it raises real questions on privacy protection, which greatly limits the use of cloud computing. However, fully homomorphic encryption (FHE) can make cloud computing consistent with privacy. In this paper, we propose a simpler FHE scheme based on ring LWE problem, with a smaller size of ciphertext and a lower noise-expansion factor for homomorphic multiplication. Then based on our optimized RLWE-based FHE scheme, we propose a fast single-database private information retrieval protocol, combining with batching and number theoretic transform technology.

show abstract

“…Halevi and Shoup [21][22][23] continuously and roundly optimized BGV-type scheme in the algorithm implementation aspect. Castryck, Iliashenko, and Vercauteren [24] improved parallel performance to packet more data within one instruction. Öztürk, Doröz, Savas and Sunar [25] designed a custom hardware accelerator to improve the performance of polynomial multiplier.…”

Section: Introductionmentioning

confidence: 99%

An Efficient BGV-type Encryption Scheme for IoT Systems

Yuan

Gao

2020

Applied Sciences

View full text Add to dashboard Cite

Internet of Thing (IoT) systems usually have less storage and computing power than desktop systems. This paper proposes an efficient BGV-type homomorphic encryption scheme in order fit for secure computing on IoT system. Our scheme reduces the storage space for switch keys and ciphertext evaluation time comparing with previous BGV-type cryptosystems. Specifically, the switch key in homomorphic computations can be a constant but no longer one for each level. Moreover, the product of two ciphertexts can be at the same sublayer as them and the multiplication operations can be repeated between two sublayers. As a result, the multiplication times will not be limited by L in an L-level circuit and, thus, the ciphertext evaluation time will decrease significantly. We implement the scheme with the C language. The performance test shows that the efficiency of the improved scheme is better than Helib in same configurations.

show abstract

Homomorphic SIM$$^2$$D Operations: Single Instruction Much More Data

Cited by 9 publications

References 24 publications

Privacy-preserving logistic regression training

Privacy-preserving logistic regression training

One Improved RLWE-based FHE and Fast Private Information Retrieval

An Efficient BGV-type Encryption Scheme for IoT Systems

Contact Info

Product

Resources

About