An end-to-end framework for privacy risk assessment of AI models

Abigail Goldsteen, Shlomit Shachor, Natalia Razinkov

Abstract: We present a first-of-a-kind end-to-end framework for running 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.