2 papers
cs.CR2026
Secure and Privacy-Preserving Vertical Federated Learning
Shan Jin, Sai Rahul Rachuri, Yizhen Wang +2
We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, fo…
cs.CR2025
Privacy Vulnerabilities in Marginals-based Synthetic Data
Steven Golob, Sikha Pentyala, Anuar Maratkhan +1
When acting as a privacy-enhancing technology, synthetic data generation (SDG) aims to maintain a resemblance to the real data while excluding personally-identifiable information.…