Dataset Securit… (2020-12-18T00:00:00.000000Z)

TL;DR

The goal of this work is to systematically categorize and discuss a wide range of dataset vulnerabilities and exploits, approaches for defending against these threats, and an array of open problems in this space.

Abstract

As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance. The absence of trustworthy human supervision over the data collection process exposes organizations to security vulnerabilities; training data can be manipulated to control and degrade the downstream behaviors of learned models. The goal of this work is to systematically categorize and discuss a wide range of dataset vulnerabilities and exploits, approaches for defending against these threats, and an array of open problems in this space.

Authors

Micah Goldblum

6 papers

Dimitris Tsipras

4 papers

Chulin Xie

1 papers

TL;DR

Abstract

Authors

References207 items

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