SIMBA: Split Inference - Mechanisms, Benchmarks and Attacks

Abhishek Singh*

Vivek Sharma

Ramesh Raskar*

Rohan Sukumaran

John Mose

Jeffrey Chiu

Justin Yu

* External authors

ECCV-24

2024

Abstract

In this work, we tackle the question of how to benchmark reconstruction of inputs from deep neural networks (DNN) representations. This inverse problem is of great importance in the privacy community where obfuscation of features has been proposed as a technique for privacy-preserving machine learning (ML) inference. In this benchmark, we characterize different obfuscation techniques and design different attack models. We propose multiple reconstruction techniques based upon distinct background knowledge of the adversary. We develop a modular platform that integrates different obfuscation techniques, reconstruction algorithms, and evaluation metrics under a common framework. Using our platform, we benchmark various obfuscation and reconstruction techniques for evaluating their privacy-utility trade-off. Finally, we release a dataset of obfuscated representations to foster research in this area.