FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework

09/05/2020
by   Pei Fang, et al.
14

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques and is a key step to improve the performance of machine learning algorithms. In the multi-party feature engineering scenario (features are stored in many different IoT devices), direct and unlimited multivariate feature transformations will quickly exhaust memory, power, and bandwidth of devices, not to mention the security of information threatened. Given this, we present a framework called FLFE to conduct privacy-preserving and communication-preserving multi-party feature transformations. The framework pre-learns the pattern of the feature to directly judge the usefulness of the transformation on a feature. Explored the new useful feature, the framework forsakes the encryption-based algorithm for the well-designed feature exchange mechanism, which largely decreases the communication overhead under the premise of confidentiality. We made experiments on datasets of both open-sourced and real-world thus validating the comparable effectiveness of FLFE to evaluation-based approaches, along with the far more superior efficacy.

READ FULL TEXT
research
04/28/2023

A Brief Study of Privacy-Preserving Practices (PPP) in Data Mining

Data mining is the way toward mining fascinating patterns or information...
research
06/22/2022

Multi-party Secure Broad Learning System for Privacy Preserving

Multi-party learning is an indispensable technique for improving the lea...
research
09/22/2020

Privacy Preserving K-Means Clustering: A Secure Multi-Party Computation Approach

Knowledge discovery is one of the main goals of Artificial Intelligence....
research
02/06/2020

Privacy Preserving PCA for Multiparty Modeling

In this paper, we present a general multiparty model-ing paradigm with P...
research
05/24/2019

Federated Forest

Most real-world data are scattered across different companies or governm...
research
12/17/2018

Privacy-Preserving Distributed Joint Probability Modeling for Spatial-Correlated Wind Farms

Building the joint probability distribution (JPD) of multiple spatial-co...
research
04/28/2023

Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm

These days, investigations of information are becoming essential for var...

Please sign up or login with your details

Forgot password? Click here to reset