Privacy

DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

A novel tool to locally engineer prompts for cloud model.

Understanding Deep Gradient Leakage via Inversion Influence Functions

We propose a new metric to efficiently evaluate the privacy risks from gradient inversion and provides new insights.

A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending …

Holistic Trustworthy ML

Instead of isolated properties, we target on a holistic trustworthiness covering every properties in one solution.

Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling

We propose a new privacy-preserving learning framework, outsourcing training to cloud without uploading data, which provides more data without injecting noise into gradient or samples.

Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent

Protecting privacy in learning while maintaining the model performance has become increasingly critical in many applications that involve sensitive data. Private Gradient Descent (PGD) is a commonly used private learning framework, which noises …

Learning Model-Based Privacy Protection under Budget Constraints

Protecting privacy in gradient-based learning has become increasingly critical as more sensitive information is being used. Many existing solutions seek to protect the sensitive gradients by constraining the overall privacy cost within a constant …