activity
20192026
most citedClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models

1 citations · 1 across the 10 of their papers we have counts for

collaborators

12 papers

cs.CR2026

CPInj: Uncovering Prompt Injection Risks in Textual Collaborative Prompt Optimization

Xinting Liao, Behnoosh Zamanlooy, Masoumeh Shafieinejad +4

Textual Collaborative Prompt Optimization (TCPO) extends TextGrad (Yuksekgonul et al., 2025) to a decentralized setting by allowing multiple clients to jointly improve prompts for…

cs.LG2026

FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models

Abtin Mahyar, Masoumeh Shafieinejad, Yuhan Liu +1

Diffusion models are the leading approach for tabular data synthesis and are increasingly used to share sensitive records. Whether they actually protect privacy has become a pressi…

cs.LG2026

On Privacy in Data-Space Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics

Masoumeh Shafieinejad, D. B. Emerson, Behnoosh Zamanlooy +5

Tabular data plays an important role in many fields and industries, including those with elevated privacy considerations and risks. As such, there is a rising interest in generatin…

cs.LG2026

MIDST Challenge at SaTML 2025: Membership Inference over Diffusion-models-based Synthetic Tabular data

Masoumeh Shafieinejad, Xi He, Mahshid Alinoori +6

Synthetic data is often perceived as a silver-bullet solution to data anonymization and privacy-preserving data publishing. Drawn from generative models like diffusion models, synt…

cs.CR2026

CAPID: Context-Aware PII Detection for Question-Answering Systems

Mariia Ponomarenko, Sepideh Abedini, Masoumeh Shafieinejad +3

Detecting personally identifiable information (PII) in user queries is critical for ensuring privacy in question-answering systems. Current approaches mainly redact all PII, disreg…

cs.CR2025

MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction

Sepideh Abedini, Shubhankar Mohapatra, D. B. Emerson +3

Large language models (LLMs) have shown promising performance on tasks that require reasoning, such as text-to-SQL, code generation, and debugging. However, regulatory frameworks w…