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Sayedeh Leila Noorbakhsh

2 accepted papers

2025

Learning Robust and Privacy-Preserving Representations via Information Theory

AAAI 2025technical

Machine learning models are vulnerable to both security attacks (e.g., adversarial examples) and privacy attacks (e.g., private attribute inference). We take the first step to mitigate both the security and privacy attacks, and maintain task utility as well. Particularly, we propose an information-t…

2024

Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks

AAAI 2024technical

Federated learning (FL) has been widely studied recently due to its property to collaboratively train data from different devices without sharing the raw data. Nevertheless, recent studies show that an adversary can still be possible to infer private information about devices' data, e.g., sensitiv…

Cited by 16SourcePDFScholar