activity
20192022
most citedBoosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing

54 citations · 118 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CR2022★ 1 cited

Backdoor Defense with Machine Unlearning

Yang Liu, Mingyuan Fan, Cen Chen +4

Backdoor injection attack is an emerging threat to the security of neural networks, however, there still exist limited effective defense methods against the attack. In this paper,…

cs.CR2020★ 2 cited

Pocket Diagnosis: Secure Federated Learning against Poisoning Attack in the Cloud

Zhuoran Ma, Jianfeng Ma, Yinbin Miao +3

Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e. Data Islands (DIs)). Ho…

cs.CR2020★ 2 cited

VerifyTL: Secure and Verifiable Collaborative Transfer Learning

Zhuoran Ma, Jianfeng Ma, Yinbin Miao +4

Getting access to labelled datasets in certain sensitive application domains can be challenging. Hence, one often resorts to transfer learning to transfer knowledge learned from a…

cs.LG2020★ 3 cited

Learn to Forget: Machine Unlearning via Neuron Masking

Yang Liu, Zhuo Ma, Ximeng Liu +5

Nowadays, machine learning models, especially neural networks, become prevalent in many real-world applications.These models are trained based on a one-way trip from user data: as…

cs.CR2020★ 47 cited

Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning

Zhuo Ma, Haoran Ge, Zhuzhu Wang +2

Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts fo…

cs.CR2019★ 8 cited

Revocable Federated Learning: A Benchmark of Federated Forest

Yang Liu, Zhuo Ma, Ximeng Liu +3

A learning federation is composed of multiple participants who use the federated learning technique to collaboratively train a machine learning model without directly revealing the…

cs.CR2019★ 54 cited

Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing

Yang Liu, Zhuo Ma, Ximeng Liu +3

Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE)…

cs.CR2019★ 1 cited

Challenges in Covert Wireless Communications with Active Warden on AWGN channels

Zhihong Liu, Jiajia Liu, Yong Zeng +3

Covert wireless communication or low probability of detection (LPD) communication that employs the noise or jamming signals as the cover to hide user's information can prevent a wa…