Securer and Faster Privacy-Preserving Distributed Machine Learning Based on MKTFHE

Основные понятия

MKTFHE enables secure distributed machine learning with improved efficiency and accuracy.

Аннотация

The content discusses the implementation of a privacy-preserving distributed machine learning framework based on MKTFHE. It covers the development of a secure decryption protocol, a new activation function, and the training of logistic regression and neural networks. The experiments show improved efficiency and accuracy compared to traditional methods.
Abstract:

MKTFHE addresses privacy concerns in distributed machine learning.
Challenges in implementing non-linear functions like Sigmoid are discussed.
Proposed solutions include secret sharing for secure decryption and a new activation function.
Efficiency and accuracy improvements demonstrated through experiments.
Introduction:

Transition from centralized to distributed systems in machine learning poses privacy challenges.
Fully homomorphic encryption (FHE) is used for private computations.
Multi-key FHE (MKFHE) allows users to encrypt data under their own keys securely.
Data Extraction:

"In recent years, multi-key fully homomorphic encryption over the torus (MKTFHE) has attracted significant attention from researchers."
"To overcome this limitation, we borrow the SecureML idea of replacing the non-linear activation function with a piecewise function."
"Our contributions can be summarized as follows: We develop a secure distributed decryption protocol for MKTFHE by introducing a secret sharing scheme."
Quotations:

"We design a new MKTFHE-friendly activation function via homogenizer and compare quads."
"Experimental results show that our function’s efficiency is 10 times higher than using 7-order Taylor polynomials directly."

Статистика

In recent years, multi-key fully homomorphic encryption over the torus (MKTFHE) has attracted significant attention from researchers.
To overcome this limitation, we borrow the SecureML idea of replacing the non-linear activation function with a piecewise function.
Our contributions can be summarized as follows: We develop a secure distributed decryption protocol for MKTFHE by introducing a secret sharing scheme.

Цитаты

"We design a new MKTFHE-friendly activation function via homogenizer and compare quads."
"Experimental results show that our function’s efficiency is 10 times higher than using 7-order Taylor polynomials directly."

Ключевые выводы из

SFPDML

by Hongxiao Wan... в arxiv.org 03-20-2024

https://arxiv.org/pdf/2211.09353.pdf

Дополнительные вопросы

How does the proposed activation function impact model accuracy?

The proposed activation function in the context is designed to be MKTFHE-friendly, allowing for efficient computation within the privacy-preserving framework. In terms of model accuracy, using this new activation function has shown promising results. When compared to traditional methods like using a 7-order Taylor polynomial or a 3-order Taylor polynomial as an activation function, the proposed function demonstrates competitive accuracy levels. The experiments conducted indicate that while there may be slight differences in accuracy compared to higher-order polynomials, the efficiency gains are significant. This means that by using the proposed activation function, one can achieve comparable levels of accuracy with improved computational efficiency.

What are potential security risks associated with multi-key decryption?

Multi-key decryption introduces certain security risks that need to be addressed in order to maintain data privacy and integrity within distributed machine learning frameworks based on encryption schemes like MKTFHE. One such risk is related to information leakage during decryption processes. For example, if not properly implemented, there could be vulnerabilities that allow adversaries to obtain partial decryption results and potentially reconstruct sensitive information about individual participants' secret keys.
Additionally, there may be concerns regarding collusion between participants or external adversaries attempting unauthorized access through compromised keys or decrypted data fragments. Without robust protocols for secure distributed decryption, there is a risk of exposing confidential information and compromising the overall privacy-preserving mechanisms employed in multi-key homomorphic encryption systems.

How can these techniques be applied to other types of machine learning models?

The techniques discussed in the context provide valuable insights into enhancing privacy-preserving distributed machine learning based on homomorphic encryption schemes like MKTFHE. These techniques can also be extended and applied to various other types of machine learning models beyond logistic regression and neural networks.
For instance:

Support for Different Activation Functions: The concept of designing MKTFHE-friendly activation functions can be adapted for use in different types of models requiring non-linear transformations.

Arithmetic Operators: Techniques developed for implementing arithmetic operators efficiently within encrypted environments can benefit a wide range of algorithms involving mathematical computations.

Distributed Decryption Protocols: Secure distributed decryption protocols can enhance confidentiality across diverse ML applications where multiple parties collaborate without revealing their private inputs.

Data Preprocessing Methods: Strategies used for preprocessing input data before encryption could find application in various ML tasks where sensitive data needs protection during processing.

By customizing these techniques according to specific requirements and constraints of different ML models, it is possible to extend their utility across a broad spectrum of applications while ensuring robust security measures are maintained throughout computations involving encrypted data sets.