Monday / CS Seminar

November 3rd/ 12:00 PM

Hybrid / Mudd 3514

Speaker

Aloni Cohen, University of Chicago

Talk Title

Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models

Abstract

"Are there any conditions under which a generative model's outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak [ICML 2023]. They define near access-freeness (NAF) and propose it as sufficient for protection.

This talk will revisit the question and establish new foundations for provable copyright protection---foundations that are firmer both technically and legally. We show that NAF models can enable verbatim copying, a blatant failure of copy protection. We give definitions providing meaningful guarantees, allowing a user to control their risk of copying by behaving in a way that is unlikely to copy in a counterfactual "clean-room setting." Finally, we formalize a common intuition about differential privacy and copyright by proving that DP implies clean-room copy protection when the dataset is "golden," a copyright deduplication requirement."

Biography

Aloni Cohen an Assistant Professor of Computer Science and Data Science at the University of Chicago. His research explores the interplay between theoretical cryptography, privacy, law, and policy. Aloni earned his PhD in the Cryptography and Information Security group at MIT, advised by Shafi Goldwasser, and was a postdoc at Boston University, joint at the Hariri Institute for Computing and the School of Law.

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