Search Results for author: Jianfeng Ma

Found 8 papers, 2 papers with code

SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark

1 code implementation12 Jul 2023 Jun Niu, Xiaoyan Zhu, Moxuan Zeng, Ge Zhang, Qingyang Zhao, Chunhui Huang, Yangming Zhang, Suyu An, Yangzhong Wang, Xinghui Yue, Zhipeng He, Weihao Guo, Kuo Shen, Peng Liu, Yulong Shen, Xiaohong Jiang, Jianfeng Ma, Yuqing Zhang

We have identified three principles for the proposed "comparing different MI attacks" methodology, and we have designed and implemented the MIBench benchmark with 84 evaluation scenarios for each dataset.

Gradient Leakage Defense with Key-Lock Module for Federated Learning

1 code implementation6 May 2023 Hanchi Ren, Jingjing Deng, Xianghua Xie, Xiaoke Ma, Jianfeng Ma

Our proposed learning method is resistant to gradient leakage attacks, and the key-lock module is designed and trained to ensure that, without the private information of the key-lock module: a) reconstructing private training data from the shared gradient is infeasible; and b) the global model's inference performance is significantly compromised.

Federated Learning Privacy Preserving

Privacy-preserving Generative Framework Against Membership Inference Attacks

no code implementations11 Feb 2022 Ruikang Yang, Jianfeng Ma, Yinbin Miao, Xindi Ma

Membership inference attacks can measure the model leakage of source data to a certain degree.

Privacy Preserving

Backdoor Defense with Machine Unlearning

no code implementations24 Jan 2022 Yang Liu, Mingyuan Fan, Cen Chen, Ximeng Liu, Zhuo Ma, Li Wang, Jianfeng Ma

First, trigger pattern recovery is conducted to extract the trigger patterns infected by the victim model.

backdoor defense Machine Unlearning

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

no code implementations23 Sep 2020 Zhuoran Ma, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng

Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model.

Cryptography and Security

Cloud-based Federated Boosting for Mobile Crowdsensing

no code implementations9 May 2020 Zhuzhu Wang, Yilong Yang, Yang Liu, Ximeng Liu, Brij B. Gupta, Jianfeng Ma

In this paper, we propose a secret sharing based federated learning architecture FedXGB to achieve the privacy-preserving extreme gradient boosting for mobile crowdsensing.

Federated Learning General Classification +3

Learn to Forget: Machine Unlearning via Neuron Masking

no code implementations24 Mar 2020 Yang Liu, Zhuo Ma, Ximeng Liu, Jian Liu, Zhongyuan Jiang, Jianfeng Ma, Philip Yu, Kui Ren

To this end, machine unlearning becomes a popular research topic, which allows users to eliminate memorization of their private data from a trained machine learning model. In this paper, we propose the first uniform metric called for-getting rate to measure the effectiveness of a machine unlearning method.

BIG-bench Machine Learning Federated Learning +2

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