Abstract
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.
Highlights
IntroductionHomomorphic encryption based on the learning with error (LWE) scheme [1] ensures high-level security even in the postquantum computing environment
From this point of view, our key contributions of this study are summarized as follows: (i) We propose a novel approach of implementing the most accurate homomorphic sigmoid function among the existing literature from the basis of bitwise operations (ii) We present t-BMPNet—a general framework for designing a multilayer perceptron neural network over a fully homomorphic encryption scheme that performs training in the encrypted domain (iii) We propose a trainable FHE neural network under the minimum interaction between the client and the server compared to other FHE trainable neural networks (iv) Our approach broadens the horizon of feasibility in an application to various deep learning studies that require a secure cloud computing model
We presented t-BMPNet that can evaluate multilayer perceptron neural net over fully homomorphic encryption scheme from the basis of bitwise operations
Summary
Homomorphic encryption based on the learning with error (LWE) scheme [1] ensures high-level security even in the postquantum computing environment. Us, by using homomorphic encryption, one can construct an environment that is both secure and private, since no information about the data being leaked to an adversary based on these properties. Some global companies and institutions have strived to construct a system to provide secure and privacy-preserving services to the clients. Homomorphic encryption enables constructing the “magic box,” in which the encrypted data are being processed without revealing any information to the cloud. In some homomorphic encryption schemes, the client is unable to retrieve any information about the design of the circuit from the cloud. Erefore, homomorphic encryption can provide privacy for both the cloud and the client In some homomorphic encryption schemes, the client is unable to retrieve any information about the design of the circuit from the cloud. erefore, homomorphic encryption can provide privacy for both the cloud and the client
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
