Defending against membership inference attacks in federated learning via adversarial example

Paper

Xie, Yuanyuan, Chen, Bing, Zhang, Jiale and Wu, Di. 2021. "Defending against membership inference attacks in federated learning via adversarial example." 2021 17th International Conference on Mobility, Sensing and Networking (MSN). Exeter, United Kingdom 13 - 15 Dec 2021 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/MSN53354.2021.00036

Paper/Presentation Title	Defending against membership inference attacks in federated learning via adversarial example
Presentation Type	Paper
Authors	Xie, Yuanyuan, Chen, Bing, Zhang, Jiale and Wu, Di
Journal or Proceedings Title	Proceedings of 2021 17th International Conference on Mobility, Sensing and Networking (MSN)
Journal Citation	pp. 153-160
Number of Pages	8
Year	2021
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Place of Publication	United States
Digital Object Identifier (DOI)	https://doi.org/10.1109/MSN53354.2021.00036
Web Address (URL) of Paper	https://ieeexplore.ieee.org/abstract/document/9751527
Web Address (URL) of Conference Proceedings	https://ieeexplore.ieee.org/xpl/conhome/9751460/proceeding
Conference/Event	2021 17th International Conference on Mobility, Sensing and Networking (MSN)
Event Details	2021 17th International Conference on Mobility, Sensing and Networking (MSN) Parent International Conference on Mobility, Sensing and Networking Delivery In person Event Date 13 to end of 15 Dec 2021 Event Location Exeter, United Kingdom
Abstract	Federated learning has attracted attention in recent years due to its native privacy-preserving features. However, it is still vulnerable to various membership inference attacks, such as backdoor, poisoning, and adversarial attacks. Membership Inference attack aims to discover the data used to train the model, which leads to privacy leaking ramifications on participants who use their local data to train the shared model. Recent research on countermeasure methods mainly focuses on protecting the parameters and has limitations in guaranteeing privacy while restraining the loss of the model. This paper proposes Fedefend, which applies adversarial examples to defend against membership inference attacks in federated learning. The proposed approach adds well-designed noise to the attack features of the target model of each iteration becomes an adversarial example. In addition, we also consider the utility loss of the model and use an adversarial method to generate noise to constrain the loss to a certain extent, which efficiently achieves a trade-off between privacy security and loss of the federated learning model. We evaluate the proposed Fedefend on two benchmark datasets, and the experimental results demonstrate that Fedefend has a good performance.
Contains Sensitive Content	Does not contain sensitive content
ANZSRC Field of Research 2020	460407. System and network security
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Byline Affiliations	Nanjing University of Aeronautics and Astronautics, China
	Yangzhou University, China
	Deakin University

Permalink -

https://research.usq.edu.au/item/z4y17/defending-against-membership-inference-attacks-in-federated-learning-via-adversarial-example

24
total views
0
total downloads
3
views this month
0
downloads this month

Export as

Related outputs

BADFSS: Backdoor Attacks on Federated Self-Supervised Learning

Zhang, Jiale, Zhu, Chengcheng, Wu, Di, Sun, Xiaobing, Yong, Jianming and Long, Guodong. 2024. "BADFSS: Backdoor Attacks on Federated Self-Supervised Learning." Larson, Kate (ed.) 33rd International Joint Conference on Artificial Intelligence (IJCAI-24). Jeju, Korea 03 - 09 Aug 2024 Korea. https://doi.org/10.24963/ijcai.2024/61

From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding

Cai, Borui, Xiang, Yong, Gao, Longxiang, Wu, Di, Zhang, He, Jin, Jiong and Luan, Tom. 2024. "From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding." IEEE Transactions on Knowledge and Data Engineering. 36 (12), pp. 8341-8348. https://doi.org/10.1109/TKDE.2024.3437479

EXVUL: Towards Effective and Explainable Vulnerability Detection for IoT Devices

Cao, Sicong, Sun, Xiaobing, Liu, Wei, Wu, Di, Zhang, Jiale, Li, Yan, Luan, Tom H. and Gao, Longxiang. 2024. "EXVUL: Towards Effective and Explainable Vulnerability Detection for IoT Devices." IEEE Internet of Things Journal. 11 (12), pp. 22385-22398. https://doi.org/10.1109/JIOT.2024.3381641

Robust equivalent circuit model parameters identification scheme for State of Charge (SOC) estimation based on maximum correntropy criterion

Zhang, Kexin, Zhao, Xuezhuan, Chen, Yu, Wu, Di, Cai, Taotao, Wang, Yi, Li, Lingling and Zhang, Ji. 2024. "Robust equivalent circuit model parameters identification scheme for State of Charge (SOC) estimation based on maximum correntropy criterion." International Journal of Electrochemical Science. 19 (5). https://doi.org/10.1016/j.ijoes.2024.100558

FedInverse: Evaluating Privacy Leakage in Federated Learning

Wu, Di, Bai, Jun, Song,Yiliao, Chen, Junjun, Zhou, Wei, Xiang, Yong and Sajjanhar, Atul. 2024. "FedInverse: Evaluating Privacy Leakage in Federated Learning." The Twelfth International Conference on Learning Representations. Vienna, Austria 07 - 11 May 2024 Austria.

Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

Rao, Bosen, Zhang, Jiale, Wu, Di, Zhu, Chengcheng, Sun, Xiaobing and Chen, Bing. 2024. "Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey." IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2024.3363670

VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems

Zhang, Jiale, Liu, Yue, Wu, Di, Lou, Shuai, Chen, Bing and Yu, Shui. 2023. "VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems." Digital Communications and Networks. 9 (4), pp. 981-989. https://doi.org/10.1016/j.dcan.2022.05.010

Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things

Wang, Nai, Chen, Junjun, Wu, Di, Yang, Wencheng, Xiang, Yong and Sajjanhar, Atul. 2023. "Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things." Journal of Information Security and Applications. 75. https://doi.org/10.1016/j.jisa.2023.103483

Campus Network Intrusion Detection based on Federated Learning

Chen, Junjun, Guo, Qiang, Fu, Zhongnan, Shang, Qun, Ma, Hao and Wu, Di. 2022. "Campus Network Intrusion Detection based on Federated Learning." 2022 International Joint Conference on Neural Networks (IJCNN). Padua, Italy 18 - 23 Jul 2022 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN55064.2022.9892843

From distributed machine learning to federated learning: In the view of data privacy and security

Shen, Sheng, Zhu, Tianqing, Wu, Di, Wang, Wei and Zhou, Wanlei. 2022. "From distributed machine learning to federated learning: In the view of data privacy and security." Concurrency and Computation: Practice and Experience. 34 (16). https://doi.org/10.1002/cpe.6002

A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning

Wu, Di, Wang, Nai, Zhang, Jiale, Zhang, Yuan, Xiang, Yong and Gao, Longxiang. 2022. "A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning." 2022 International Joint Conference on Neural Networks (IJCNN). Padua, Italy 18 - 23 Jul 2022 IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN55064.2022.9892039

Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks

Zhao, Ying, Chen, Junjun, Zhang, Jiale, Wu, Di, Blumenstein, Michael and Yu, Shui. 2022. "Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks." Concurrency and Computation: Practice and Experience. 34 (7). https://doi.org/10.1002/cpe.5906

Lightweight Mutual Authentication Scheme Enabled by Stateless Blockchain for UAV Networks

Kong, Lingjun, Chen, Bing, Hu, Feng and Zhang, Ji. 2022. "Lightweight Mutual Authentication Scheme Enabled by Stateless Blockchain for UAV Networks." Security and Communication Networks. 2022. https://doi.org/10.1155/2022/2330052

Relational intelligence recognition in online social networks - a survey

Zhang, Ji, Tan, Leonard, Tao, Xiaohui, Pham, Thuan and Chen, Bing. 2020. "Relational intelligence recognition in online social networks - a survey." Computer Science Review. 35. https://doi.org/10.1016/j.cosrev.2019.100221

Defending poisoning attacks in federated learning via adversarial training method

Zhang, Jiale, Wu, Di, Liu, Chengyong and Chen, Bing. 2020. "Defending poisoning attacks in federated learning via adversarial training method." 3rd International Conference on Frontiers in Cyber Security (FCS 2020). Tianjin, China 15 - 17 Nov 2020 Singapore . Springer. https://doi.org/10.1007/978-981-15-9739-8_7

An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition

Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2019. "An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition." 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). Auckland, New Zealand 19 - 21 Nov 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IVCNZ.2018.8634660

Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos

Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2019. "Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos." 2018 Digital Image Computing: Techniques and Applications (DICTA). Canberra, Australia 10 - 13 Dec 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/DICTA.2018.8615804

Adversarial action data augmentation for similar gesture action recognition

Wu, Di, Chen, Junjun, Sharma, Nabin, Pan, Shirui, Long, Guodong and Blumenstein, Michael. 2019. "Adversarial action data augmentation for similar gesture action recognition." 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary 14 - 19 Jul 2019 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2019.8851993

Feature-dependent graph convolutional autoencoders with adversarial training methods

Wu, Di, Hu, Ruiqi, Zheng, Yu, Jiang, Jing, Sharma, Nabin and Blumenstein, Michael. 2019. "Feature-dependent graph convolutional autoencoders with adversarial training methods." 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary 14 - 19 Jul 2019 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2019.8852314

Poisoning attack in federated learning using generative adversarial nets

Zhang, Jiale, Chen, Junjun, Wu, Di, Chen, Bing and Yu, Shui. 2019. "Poisoning attack in federated learning using generative adversarial nets." 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). Rotorua, New Zealand 05 - 08 Aug 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/TrustCom/BigDataSE.2019.00057

Network anomaly detection by using a time-decay closed frequent pattern

Zhao, Ying, Chen, Junjun, Wu, Di, Teng, Jian, Sharma, Nabin, Sajjanhar, Atul and Blumenstein, Michael. 2019. "Network anomaly detection by using a time-decay closed frequent pattern." Information (Basel). 10 (8). https://doi.org/10.3390/info10080262

Multi-task network anomaly detection using federated learning

Zhao, Ying, Chen, Junjun, Wu, Di, Teng, Jian and Yu, Shui. 2019. "Multi-task network anomaly detection using federated learning." 10th international symposium on information and communication technology (SoICT 2019). Hanoi, Viet Nam 04 - 06 Dec 2019 United States. Association for Computing Machinery (ACM). https://doi.org/10.1145/3368926.3369705

A privacy-preserving access control scheme with verifiable and outsourcing capabilities in fog-cloud computing

Cheng, Zhen, Zhang, Jiale, Qian, Hongyan, Xiang, Mingrong and Wu, Di. 2019. "A privacy-preserving access control scheme with verifiable and outsourcing capabilities in fog-cloud computing." 19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2019). Melbourne, Australia 09 - 11 Dec 2019 Switzerland . Springer. https://doi.org/10.1007/978-3-030-38991-8_23

Robust feature-based automated multi-view human action recognition system

Chou, Kuang-Pen, Prasad, Mukesh, Wu, Di, Sharma, Nabin, Li, Dong-Lin, Lin, Yu-Feng, Blumenstein, Michael, Lin, Wen-Chieh and Lin, Chin-Teng. 2018. "Robust feature-based automated multi-view human action recognition system." IEEE Access. 6, pp. 15283-15296. https://doi.org/10.1109/ACCESS.2018.2809552

Recent advances in video-based human action recognition using deep learning: A review

Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2017. "Recent advances in video-based human action recognition using deep learning: A review." 2017 International Joint Conference on Neural Networks (IJCNN). Anchorage, United States 14 - 19 May 2017 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2017.7966210

On addressing the imbalance problem: a correlated KNN approach for network traffic classification

Wu, Di, Chen, Xiao, Chen, Chao, Zhang, Jun, Xiang, Yang and Zhou, Wanlei. 2015. "On addressing the imbalance problem: a correlated KNN approach for network traffic classification." NSS 2014: 8th International Conference on Network and System Security. Xi'an, China 15 - 17 Oct 2014 Switzerland . Springer. https://doi.org/10.1007/978-3-319-11698-3_11

Detecting stepping stones by abnormal causality probability

Wen, Sheng, Wu, Di, Li, Ping, Xiang, Yang, Zhou, Wanlei and Wei, Guiyi. 2015. "Detecting stepping stones by abnormal causality probability." Security and Communication Networks. 8 (10), pp. 1831-1844. https://doi.org/10.1002/sec.1037

A Survey on Latest Botnet Attack and Defense

Zhang, Lei, Yu, Shui, Wu, Di and Watters, Paul. 2011. "A Survey on Latest Botnet Attack and Defense ." 10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2011). Changsha, China 16 - 18 Nov 2011 China. https://doi.org/10.1109/TrustCom.2011.11