Augmented Dual-Shuffle-based Moving Target Defense to Ensure CIA-triad in Federated Learning

Zhou, Zan; Xu, Changqiao; Wang, Mingze; Ma, Tengchao; Yu, Shui

doi:10.1109/globecom46510.2021.9685154

Cited by 4 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, the added dynamics consequently reduce the likelihood of successful attacks and increase the system resiliency while limiting the disclosure of system vulnerabilities and opportunities for attacks [40]. Zhou et al [59] introduced ADS-MTD, a double-shuffle system comprising model and client-shuffling components. Specifically, MTD takes place in the second phase as a hierarchical multi-shuffler structure to dynamically and efficiently assess and eliminate malicious FL participants to enhance the aggregated model's integrity and availability.…”

Section: Moving Target Defensementioning

confidence: 99%

“…United and Non-orthogonal Defense techniques All defensive methods are not mutually exclusive; a single defense technique can thwart multiple attacks. Nevertheless, existing countermeasures are mostly studied separately and orthogonal [59]. For example, some encryption-based methods come at a high communication cost, causing communication bottlenecks, and perturbation-based methods sacrifice performance.…”

Section: Future Research Directionmentioning

confidence: 99%

See 1 more Smart Citation

Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare

Jeong

Chung

2022

Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications

View full text Add to dashboard Cite

The advent of Federated Learning has enabled the creation of a high-performing model as if it had been trained on a considerable amount of data. A multitude of participants and a server cooperatively train a model without the need for data disclosure or collection. The healthcare industry, where security and privacy are paramount, can substantially benefit from this new learning paradigm, as data collection is no longer feasible due to stringent data policies. Nonetheless, unaddressed challenges and insufficient attack mitigation are hampering its adoption. Attack surfaces differ from traditional centralized learning in that the server and clients communicate between each round of training. In this paper, we thus present vulnerabilities, attacks, and defenses based on the widened attack surfaces, as well as suggest promising new research directions toward a more robust FL.

show abstract

Section: Moving Target Defensementioning

confidence: 99%

Section: Future Research Directionmentioning

confidence: 99%

Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare

Jeong

Chung

2022

Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications

View full text Add to dashboard Cite

show abstract

Anomaly detection and defense techniques in federated learning: a comprehensive review

Zhang,

Yang,

Mao

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

In recent years, deep learning methods based on a large amount of data have achieved substantial success in numerous fields. However, with increases in regulations for protecting private user data, access to such data has become restricted. To overcome this limitation, federated learning (FL) has been widely utilized for training deep learning models without centralizing data. However, the inaccessibility of FL data and heterogeneity of the client data render difficulty in providing security and protecting the privacy in FL. In addition, the security and privacy anomalies in the corresponding systems significantly hinder the application of FL. Numerous studies have been proposed aiming to maintain the model security and mitigate the leakage of private training data during the FL training phase. Existing surveys categorize FL attacks from a defensive standpoint, but lack the efficiency of pinpointing attack points and implementing timely defenses. In contrast, our survey comprehensively categorizes and summarizes detected anomalies across client, server, and communication perspectives, facilitating easier identification and timely defense measures. Our survey provides an overview of the FL system and briefly introduces the FL security and privacy anomalies. Next, we detail the existing security and privacy anomalies and the methods of detection and defense from the perspectives of the client, server, and communication process. Finally, we address the security and privacy anomalies in non-independent identically distributed cases during FL and summarize the related research progress. This survey aims to provide a systematic and comprehensive review of security and privacy research in FL to help understand the progress and better apply FL in additional scenarios.

show abstract

MR-FFL: A Stratified Community-Based Mutual Reliability Framework for Fairness-Aware Federated Learning in Heterogeneous UAV Networks

Zhou,

Zhuang,

et al. 2024

IEEE Internet Things J.

View full text Add to dashboard Cite

Augmented Dual-Shuffle-based Moving Target Defense to Ensure CIA-triad in Federated Learning

Cited by 4 publications

References 13 publications

Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare

Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare

Anomaly detection and defense techniques in federated learning: a comprehensive review

MR-FFL: A Stratified Community-Based Mutual Reliability Framework for Fairness-Aware Federated Learning in Heterogeneous UAV Networks

Contact Info

Product

Resources

About