SoK: Privacy-preserving Deep Learning with Homomorphic Encryption

12/23/2021
by   Robert Podschwadt, et al.
0

Outsourced computation for neural networks allows users access to state of the art models without needing to invest in specialized hardware and know-how. The problem is that the users lose control over potentially privacy sensitive data. With homomorphic encryption (HE) computation can be performed on encrypted data without revealing its content. In this systematization of knowledge, we take an in-depth look at approaches that combine neural networks with HE for privacy preservation. We categorize the changes to neural network models and architectures to make them computable over HE and how these changes impact performance. We find numerous challenges to HE based privacy-preserving deep learning such as computational overhead, usability, and limitations posed by the encryption schemes.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/18/2023

Privacy-Preserving 3-Layer Neural Network Training using Mere Homomorphic Encryption Technique

In this manuscript, we consider the problem of privacy-preserving traini...
research
11/13/2020

Practical Privacy-Preserving Data Science With Homomorphic Encryption: An Overview

Privacy has gained a growing interest nowadays due to the increasing and...
research
05/03/2023

Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference

The use of Neural Networks (NNs) for sensitive data processing is becomi...
research
09/22/2021

Privacy-preserving Credit Scoring via Functional Encryption

The majority of financial organizations managing confidential data are a...
research
07/26/2021

Fully Homomorphically Encrypted Deep Learning as a Service

Fully Homomorphic Encryption (FHE) is a relatively recent advancement in...
research
09/09/2020

A brief history on Homomorphic learning: A privacy-focused approach to machine learning

Cryptography and data science research grew exponential with the interne...
research
04/21/2019

Obfuscation for Privacy-preserving Syntactic Parsing

The goal of homomorphic encryption is to encrypt data such that another ...

Please sign up or login with your details

Forgot password? Click here to reset