Logarithm Design on Encrypted Data with Bitwise Operation

Yoo, Joon Soo; Song, Baek Kyung; Yoon, Ji Won

doi:10.1007/978-3-030-17982-3_9

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…For the sake of the flow of this study, we suggest the readers to refer to [19,23,24] for a more insightful understanding of the above operations as well as other homomorphic operations given in Table 1. e Algorithm 1 illustrates the whole process of the training in plaintext, where lines (7-10) and lines (12,15,16) are the feedforward and the backpropagation algorithms, respectively.…”

Section: Multiplicationmentioning

confidence: 99%

t-BMPNet: Trainable Bitwise Multilayer Perceptron Neural Network over Fully Homomorphic Encryption Scheme

Yoo

Yoon

2021

Security and Communication Networks

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Homomorphic encryption (HE) is notable for enabling computation on encrypted data as well as guaranteeing high-level security based on the hardness of the lattice problem. In this sense, the advantage of HE has facilitated research that can perform data analysis in an encrypted state as a purpose of achieving security and privacy for both clients and the cloud. However, much of the literature is centered around building a network that only provides an encrypted prediction result rather than constructing a system that can learn from the encrypted data to provide more accurate answers for the clients. Moreover, their research uses simple polynomial approximations to design an activation function causing a possibly significant error in prediction results. Conversely, our approach is more fundamental; we present t-BMPNet which is a neural network over fully homomorphic encryption scheme that is built upon primitive gates and fundamental bitwise homomorphic operations. Thus, our model can tackle the nonlinearity problem of approximating the activation function in a more sophisticated way. Moreover, we show that our t-BMPNet can perform training—backpropagation and feedforward algorithms—in the encrypted domain, unlike other literature. Last, we apply our approach to a small dataset to demonstrate the feasibility of our model.

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Section: Multiplicationmentioning

confidence: 99%

t-BMPNet: Trainable Bitwise Multilayer Perceptron Neural Network over Fully Homomorphic Encryption Scheme

Yoo

Yoon

2021

Security and Communication Networks

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A Bitwise Logistic Regression Using Binary Approximation and Real Number Division in Homomorphic Encryption Scheme

Yoo

Hwang

Song

et al. 2019

Lecture Notes in Computer Science

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Homomorphic Model Selection for Data Analysis in an Encrypted Domain

2020

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Secure computation, a methodology of computing on encrypted data, has become a key factor in machine learning. Homomorphic encryption (HE) enables computation on encrypted data without leaking any information to untrusted servers. In machine learning, the model selection method is a crucial algorithm that determines the performance and reduces the fitting problem. Despite the importance of finding the optimal model, none of the previous studies have considered model selection when performing data analysis through the HE scheme. The HE-based model selection we proposed finds the optimal complexity that best describes given data that is encrypted and whose distribution is unknown. Since this process requires a matrix calculation, we constructed the matrix multiplication and inverse of the matrix based on the bitwise operation. Based on these, we designed the model selection of the HE cross-validation approach and the HE Bayesian approach for homomorphic machine learning. Our focus was on evidence approximation for linear models to find goodness-of-fit that maximizes the evidence. We conducted an experiment on a dataset of age and Body Mass Index (BMI) from Kaggle to compare the capabilities and our model showed that encrypted data can regress homomorphically without decrypting it.

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Logarithm Design on Encrypted Data with Bitwise Operation

Cited by 3 publications

References 7 publications

t-BMPNet: Trainable Bitwise Multilayer Perceptron Neural Network over Fully Homomorphic Encryption Scheme

t-BMPNet: Trainable Bitwise Multilayer Perceptron Neural Network over Fully Homomorphic Encryption Scheme

A Bitwise Logistic Regression Using Binary Approximation and Real Number Division in Homomorphic Encryption Scheme

Homomorphic Model Selection for Data Analysis in an Encrypted Domain

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