Publication
SYSTOR 2022
Poster

An End-to-end Framework for Privacy Risk Assessment of AI Models

Abstract

We present a first-of-a-kind end-to-end framework for run- ning privacy risk assessments of AI models that enables assessing models from multiple ML frameworks, using a variety of low-level privacy attacks and metrics. The tool automatically selects which attacks and metrics to run based on answers to questions, runs the attacks, summarizes and visualizes the results in an easy-to-consume manner.

Date

13 Jun 2022

Publication

SYSTOR 2022

Authors

Topics

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