Work

Verifiable and Provably Secure Machine Unlearning

Year: 2025

Type: conference-paper

Source: IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)

DOI: 10.1109/satml64287.2025.00033

Authors: Thorsten Eisenhofer, Doreen Riepel, Varun Chandrasekaran, Esha Ghosh, Olga Ohrimenko, Nicolas Papernot

Institutions: Berlin Institute for the Foundations of Learning and Data, Technische Universität Berlin, Helmholtz Center for Information Security, University of Illinois Urbana-Champaign, Microsoft (United States), The University of Melbourne, University of Toronto, Vector Institute

Language: English

Topic: Cryptography and Data Security

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

OpenAlex ID: W4410609060

FWCI: 13.77

Cites: 64

Cited by: 15

Related to: 10