CORE MACHINE LEARNING

Differentially Private Representation Learning via Image Captioning

June 14, 2024

Abstract

Differentially private (DP) machine learning is considered the gold-standard solution for training a model from sensitive data while still preserving privacy. However, a major barrier to achieving this ideal is its sub-optimal privacy-accuracy trade-off, which is particularly visible in DP representation learning. Specifically, it has been shown that under modest privacy budgets, most models learn representations that are not significantly better than hand-crafted features. In this work, we show that effective DP representation learning can be done via image captioning and scaling up to internet-scale multimodal datasets. Through a series of engineering tricks, we successfully train a DP image captioner (DP-Cap) on a 233M subset of LAION-2B from scratch using a reasonable amount of computation, and obtaining unprecedented high-quality image features that can be used in a variety of downstream vision and vision-language tasks. For example, under a privacy budget of epsilon=8 for the LAION dataset, a linear classifier trained on top of learned DP-Cap features attains 65.8% accuracy on ImageNet-1K, considerably improving the previous SOTA of 56.5%. Our work challenges the prevailing sentiment that high-utility DP representation learning cannot be achieved by training from scratch. Code is available at https://github.com/facebookresearch/dpcap.

Download the Paper

AUTHORS

Written by

Tom Sander

Yaodong Yu

Maziar Sanjabi

Alain Durmus

Yi Ma

Kamalika Chaudhuri

Chuan Guo

Publisher

ICML

Research Topics

Core Machine Learning

Related Publications

July 08, 2024

THEORY

CORE MACHINE LEARNING

An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes

Antonio Orvieto, Lin Xiao

July 08, 2024

June 17, 2024

HUMAN & MACHINE INTELLIGENCE

COMPUTER VISION

D-Flow: Differentiating through Flows for Controlled Generation

Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer, Uriel Singer, Yaron Lipman

June 17, 2024

June 17, 2024

COMPUTER VISION

CORE MACHINE LEARNING

Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models

Neta Shaul, Uriel Singer, Ricky Chen, Matt Le, Ali Thabet, Albert Pumarola, Yaron Lipman

June 17, 2024

June 14, 2024

CORE MACHINE LEARNING

ViP: A Differentially Private Foundation Model for Computer Vision

Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo

June 14, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.