activity
20222026
most citedPPA: Preference Profiling Attack Against Federated Learning

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR2026

ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders

Yongqi Jiang, Yansong Gao, Boyu Kuang +3

Self-supervised learning (SSL) encoders are invaluable intellectual property (IP). However, no existing SSL watermarking for IP protection can concurrently satisfy the following tw…

cs.CR2024

Intellectual Property Protection for Deep Learning Model and Dataset Intelligence

Yongqi Jiang, Yansong Gao, Chunyi Zhou +3

With the growing applications of Deep Learning (DL), especially recent spectacular achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the commercial significan…

cs.LG2024

TruVRF: Towards Triple-Granularity Verification on Machine Unlearning

Chunyi Zhou, Anmin Fu, Zhiyang Dai

The concept of the right to be forgotten has led to growing interest in machine unlearning, but reliable validation methods are lacking, creating opportunities for dishonest model…

cs.LG2024

Decaf: Data Distribution Decompose Attack against Federated Learning

Zhiyang Dai, Chunyi Zhou, Anmin Fu

In contrast to prevalent Federated Learning (FL) privacy inference techniques such as generative adversarial networks attacks, membership inference attacks, property inference atta…

cs.LG2024

Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

Na Li, Chunyi Zhou, Yansong Gao +4

Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the right to be f…

cs.LG2022★ 1 cited

PPA: Preference Profiling Attack Against Federated Learning

Chunyi Zhou, Yansong Gao, Anmin Fu +5

Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require d…