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Poster Session 2 West
Wednesday, December 11, 2024 4:30 PM → 7:30 PM
Poster #5303

DECO-Bench: Unified Benchmark for Decoupled Task-Agnostic Synthetic Data Release

Farzaneh Askari, Lingjuan Lyu, Vivek Sharma

Abstract

In this work, we tackle the question of how to systematically benchmark task-agnostic decoupling methods for privacy-preserving machine learning (ML). Sharing datasets that include sensitive information often triggers privacy concerns, necessitating robust decoupling methods to separate sensitive and non-sensitive attributes. Despite the development of numerous decoupling techniques, a standard benchmark for systematically comparing these methods remains absent. Our framework integrates various decoupling techniques along with synthetic data generation and evaluation protocols within a unified system. Using our framework, we benchmark various decoupling techniques and evaluating their privacy-utility trade-offs. Finally, we release our source code, pre-trained models, datasets of decoupled representations to foster research in this area. The synthesized data and additional info can be found here http://tiny.cc/neurips24_decobench