Work

ERASURE: A Modular and Extensible Framework for Machine Unlearning

Year: 2025

Type: conference-paper

Abstract: Machine Unlearning (MU) is an emerging research area that enables models to selectively forget specific data, a critical requirement for privacy compliance (e.g., GDPR, CCPA) and security. However, the lack of standardized benchmarks makes evaluating and developing unlearning methods difficult. To address this gap, we introduce ERASURE, a benchmarking and development framework designed to systematically assess MU techniques. ERASURE provides a modular, extensible, open-source environment with real-world datasets and standardized unlearning measures. The framework is designed with configuration-driven workflows and an inversion of control architecture, allowing integration of new datasets, models, and evaluation measures. ERASURE advances trustworthy AI research as a tool for researchers to develop and benchmark new MU methods.

Source: ACM International Conference on Information and Knowledge Management (CIKM)

DOI: 10.1145/3746252.3761627

Authors: Andrea D'Angelo, Claudio Savelli, Gabriele Tagliente, Flavio Giobergia, Elena Baralis, Giovanni Stilo

Institutions: University of L'Aquila, Politecnico di Torino

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: European Commission

Award: G4508289328