32 citations · 91 across the 60 of their papers we have counts for
13 papers · 1 filter
Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation
Osama Zafar, Alexander Nemecek, Yiqian Zhang +5
Standard PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individuals. We introduce a Privacy Poli…
CausalGuard: Conformal Inference under Graph Uncertainty
Vikash Singh, Weicong Chen, Debargha Ganguly +12
Estimating treatment effects from observational data requires choosing an adjustment set, but valid adjustment depends on an unknown causal graph. Graph misspecification can cause…
Quantifying Memorization and Privacy Risks in Genomic Language Models
Alexander Nemecek, Wenbiao Li, Xiaoqian Jiang +2
Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction, regulatory element identificat…
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…
Comparing Reconstruction Attacks on Pretrained Versus Full Fine-tuned Large Language Model Embeddings on Homo Sapiens Splice Sites Genomic Data
Reem Al-Saidi, Erman Ayday, Ziad Kobti
This study investigates embedding reconstruction attacks in large language models (LLMs) applied to genomic sequences, with a specific focus on how fine-tuning affects vulnerabilit…
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…
Privacy-Preserving Model and Preprocessing Verification for Machine Learning
Wenbiao Li, Anisa Halimi, Xiaoqian Jiang +2
This paper presents a framework for privacy-preserving verification of machine learning models, focusing on models trained on sensitive data. Integrating Local Differential Privacy…
Privacy-Preserving Data Linkage Across Private and Public Datasets for Collaborative Agriculture Research
Osama Zafar, Rosemarie Santa Gonzalez, Gabriel Wilkins +2
Digital agriculture leverages technology to enhance crop yield, disease resilience, and soil health, playing a critical role in agricultural research. However, it raises privacy co…
Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering
Maryam Ghasemian, Erman Ayday
This study investigates the optimal selection of parameters for collaborative clustering while ensuring data privacy. We focus on key clustering algorithms within a collaborative f…
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning
Amr Abourayya, Jens Kleesiek, Kanishka Rao +4
In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been p…
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models
Abdullah Caglar Oksuz, Anisa Halimi, Erman Ayday
Explainable Artificial Intelligence (XAI) aims to uncover the decision-making processes of AI models. However, the data used for such explanations can pose security and privacy ris…
Key Protected Classification for Collaborative Learning
Mert Bülent Sarıyıldız, Ramazan Gökberk Cinbiş, Erman Ayday
Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative…
BPRS: Belief Propagation Based Iterative Recommender System
Erman Ayday, Arash Einolghozati, Faramarz Fekri
In this paper we introduce the first application of the Belief Propagation (BP) algorithm in the design of recommender systems. We formulate the recommendation problem as an infere…