A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning. - Sonakshi Garg; Sandeep Kumar 010; Pranab K. Muhuri (Comput. Biol. Medicine, 2022)

K-Anonymous Privacy Preserving Manifold Learning. - Sonakshi Garg; Vicenç Torra (SECRYPT, 2023)

Fair Differentially Private Federated Learning Framework. - Ayush K. Varshney; Sonakshi Garg; Arka Ghosh; Sargam Gupta (CoRR, 2023)

Task-Specific Knowledge Distillation with Differential Privacy in LLMs. - Sonakshi Garg; Vicenç Torra (ESORICS (2), 2024)

Exploring Distribution Learning of Synthetic Data Generators for Manifolds. - Sonakshi Garg; Vicenç Torra (ESORICS Workshops (1), 2024)

Poisoning Attacks on Federated Learning for Autonomous Driving. - Sonakshi Garg; Hugo Jönsson; Gustav Kalander; Axel Nilsson; Bhhaanu Pirange; Viktor Valadi; Johan Östman (SCAI, 2024)

Can Synthetic Data Preserve Manifold Properties? - Sonakshi Garg; Vicenç Torra (SEC, 2024)

Privacy in manifolds: Combining k-anonymity with differential privacy on Fréchet means. - Sonakshi Garg; Vicenç Torra (Comput. Secur., 2024)

A stratified review of COVID-19 infection forecasting and an efficient methodology using multiple domain-based transfer learning. - Sandeep Kumar 010; Sonakshi Garg; Pranab K. Muhuri (Expert Syst. Appl., 2025)