Privacy & Security

1. Federated Learning

Federated Learning is a distributed machine‑learning paradigm that enables collaborative multi‑party model training while preserving data privacy. Its core principle is keeping raw data stored locally. Each participant trains models locally and uploads only updated model parameters (gradient updates) to a central server for global aggregation, instead of sharing original data directly. This effectively resolves data‑silo issues. Additional safeguards including encryption, differential privacy and secure multi‑party computation further mitigate privacy‑leakage risks. It is widely adopted in privacy‑sensitive sectors such as healthcare and finance.

2. Adversarial Attacks and Defenses

Research on adversarial attacks and defenses for privacy protection prevents adversaries from inferring sensitive private information or breaking privacy‑preserving mechanisms via model outputs. Adversarial attacks may generate adversarial examples (tiny input perturbations) to mislead models and leak individual‑level features via membership‑inference or attribute‑inference attacks; gradients can even be inverted to reconstruct original training data. Defensive countermeasures include adversarial training (improving model robustness against adversarial samples), gradient perturbation or clipping (differential‑privacy techniques), encrypted intermediate parameters, and privacy‑enhanced model architectures such as GAN‑based data anonymization. This field balances model utility and security. Use cases include face recognition, medical‑data analysis and handwritten‑signature verification, which handle highly sensitive personal information.