19 citations · 36 across the 15 of their papers we have counts for
6 papers · 1 filter
SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification
Ivan Alejandro Montoya Sanchez, Anantaa Kotal, Aritran Piplai
Cybersecurity systems must adapt rapidly to emerging threats. However, labeled data for new threat categories is unavailable when those threats first appear. Generalized zero-shot…
Differentially Private Synthetic Data Generation Using Context-Aware GANs
Anantaa Kotal, Anupam Joshi
The widespread use of big data across sectors has raised major privacy concerns, especially when sensitive information is shared or analyzed. Regulations such as GDPR and HIPAA imp…
When Privacy Isn't Synthetic: Hidden Data Leakage in Generative AI Models
S. M. Mustaqim, Anantaa Kotal, Paul H. Yi
Generative models are increasingly used to produce privacy-preserving synthetic data as a safe alternative to sharing sensitive training datasets. However, we demonstrate that such…
Impugan: Learning Conditional Generative Models for Robust Data Imputation
Zalish Mahmud, Anantaa Kotal, Aritran Piplai
Incomplete data are common in real-world applications. Sensors fail, records are inconsistent, and datasets collected from different sources often differ in scale, sampling rate, a…
FAIRPLAI: A Human-in-the-Loop Approach to Fair and Private Machine Learning
David Sanchez, Holly Lopez, Michelle Buraczyk +1
As machine learning systems move from theory to practice, they are increasingly tasked with decisions that affect healthcare access, financial opportunities, hiring, and public ser…
KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation
Anantaa Kotal, Anupam Joshi
The integration of privacy measures, including differential privacy techniques, ensures a provable privacy guarantee for the synthetic data. However, challenges arise for Generativ…