DP-XGBoost: Private Machine Learning at Scale

10/25/2021
by   Nicolas Grislain, et al.
21

The big-data revolution announced ten years ago does not seem to have fully happened at the expected scale. One of the main obstacle to this, has been the lack of data circulation. And one of the many reasons people and organizations did not share as much as expected is the privacy risk associated with data sharing operations. There has been many works on practical systems to compute statistical queries with Differential Privacy (DP). There have also been practical implementations of systems to train Neural Networks with DP, but relatively little efforts have been dedicated to designing scalable classical Machine Learning (ML) models providing DP guarantees. In this work we describe and implement a DP fork of a battle tested ML model: XGBoost. Our approach beats by a large margin previous attempts at the task, in terms of accuracy achieved for a given privacy budget. It is also the only DP implementation of boosted trees that scales to big data and can run in distributed environments such as: Kubernetes, Dask or Apache Spark.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/09/2022

A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning

We review the use of differential privacy (DP) for privacy protection in...
research
12/26/2022

Packing Privacy Budget Efficiently

Machine learning (ML) models can leak information about users, and diffe...
research
05/19/2022

Differential Privacy: What is all the noise about?

Differential Privacy (DP) is a formal definition of privacy that provide...
research
07/26/2022

Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning

In this paper, we show that the process of continually learning new task...
research
07/09/2021

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation

In recent years, formal methods of privacy protection such as differenti...
research
06/29/2021

Privacy Budget Scheduling

Machine learning (ML) models trained on personal data have been shown to...
research
11/21/2022

Privacy in Practice: Private COVID-19 Detection in X-Ray Images

Machine learning (ML) can help fight the COVID-19 pandemic by enabling r...

Please sign up or login with your details

Forgot password? Click here to reset