28 citations · 49 across the 17 of their papers we have counts for
18 papers
SoK: Privacy Attacks on Machine Learning via Explainable AI
Abdullah Caglar Oksuz, Anisa Halimi, Erman Ayday
Machine learning explanations reveal model behavior beyond predictions, creating attack surfaces for model confidentiality and data privacy. We systematize 25 studies that exploit…
PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets
Anisa Halimi, Liubov Nedoshivina, Kieran Fraser +1
The growing availability of clinical data has increased the use of machine learning, yet centralized data aggregation is often infeasible for sensitive health information. Federate…
Persona-Conditioned Adversarial Prompting (PCAP): Multi-Identity Red-Teaming for Enhanced Adversarial Prompt Discovery
Cristian Morasso, Anisa Halimi, Muhammad Zaid Hameed +1
Existing automated red-teaming pipelines often miss attacks that depend on attacker identity, framing, or multi-turn tactics. This under-coverage underestimates real-world risk. We…
Persona-Conditioned Adversarial Prompting: Multi-Identity Red-Teaming for Adversarial Discovery and Mitigation
Cristian Morasso, Anisa Halimi, Muhammad Zaid Hameed +1
Automated red-teaming for LLMs often discovers narrow attack slices, missing diverse real-world threats, and yielding insufficient data for safety fine-tuning. We introduce Persona…
LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings
Abdullah Caglar Oksuz, Anisa Halimi, Erman Ayday
Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a data point was used during training. Existing MIAs often assume access to…
PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework
Weiqi Yue, Wenbiao Li, Yuzhou Jiang +3
Federated learning enables collaborative model training without sharing raw data, but data heterogeneity consistently challenges the performance of the global model. Traditional op…