Privacy preservation in federated learning: An insightful survey from the GDPR perspective

Truong, Nguyen Binh; Sun, Kai; Wang, Siyao; Guitton, Florian; Guo, Yike

doi:10.1016/j.cose.2021.102402

Cited by 180 publications

(102 citation statements)

References 30 publications

Supporting

Mentioning

100

Contrasting

Unclassified

Order By: Relevance

“…In ML-based IDS, CTI is challenging to achieve, as benign and malicious training data sets from several organisations are required for model training, which often poses privacy and security concerns. More importantly, recently introduced laws such as the General Data Protection Regulation (GDPR) [20] in Europe aim to protect data privacy and address unauthorised sharing of sensitive user information. Infraction of such laws often attracts serious legal issues and hefty fines, in the case of GDPR, the fine can be up to $20 million [21].…”

Section: Cyber Threat Intelligencementioning

confidence: 99%

HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection

Sarhan¹,

Lo²,

Layeghy³

et al. 2022

Preprint

View full text Add to dashboard Cite

The continuous strengthening of the security posture of IoT ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. The utilisation of Machine Learning (ML) capabilities in the defence against IoT cyber attacks has many potential benefits. However, the currently proposed frameworks do not consider data privacy, secure architectures, and/or scalable deployments of IoT ecosystems. In this paper, we propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. We highlight and demonstrate the importance of sharing cyber threat intelligence among inter-organisational IoT networks to improve the model's detection capabilities. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The transactions (model updates) and processes will run on a secure immutable ledger, and the conformance of executed tasks will be verified by the smart contract. We have tested our solution and demonstrated its feasibility by implementing it and evaluating the intrusion detection performance using a key IoT data set. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.

show abstract

Section: Cyber Threat Intelligencementioning

confidence: 99%

HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection

Sarhan¹,

Lo²,

Layeghy³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…1, SIC is adopted to decode model parameters of each device from the received superposition signal. In this way, BS is capable of distinguishing malicious devices who mount attacks on the system via analyzing statistic characteristics of the decoded model parameters [10]. Beyond that, memorizing aggregated global model parameters benefits the convergence of the federated learning process through techniques like momentum based stochastic gradient descent [11] as well.…”

Section: A System Modelmentioning

confidence: 99%

QoS-Constrained Federated Learning Empowered by Intelligent Reflecting Surface

Zheng¹,

Ni²,

Tian³

2021

Preprint

View full text Add to dashboard Cite

This paper investigates the model aggregation process in an over-the-air federated learning (AirFL) system, where an intelligent reflecting surface (IRS) is deployed to assist the transmission from users to the base station (BS). With the purpose of overcoming the absence of the security examination against malicious individuals, successive interference cancellation (SIC) is adopted as a basis to support analyzing statistic characteristics of model parameters from devices. The objective of this paper is to minimize the mean-square-error by jointly optimizing the receive beamforming vector at the BS, transmit power allocation at users, and phase shift matrix of the IRS, subject to the transmit power constraint for devices, unit-modulus constraint for reflecting elements, SIC decoding order constraint and quality-of-service constraint. To address this complicated problem, alternating optimization is employed to decompose it into three subproblems, where the optimal receive beamforming vector is obtained by solving the first subproblem with the Lagrange dual method. Then, the convex relaxation method is applied to the transmit power allocation subproblem to find a suboptimal solution. Eventually, the phase shift matrix subproblem is addressed by invoking the semidefinite relaxation. Simulation results validate the availability of IRS and the effectiveness of the proposed scheme in improving federated learning performance.

show abstract

“…A more challenging scenario is to combine both the data-free and multi-source settings, i.e., the source data collected from multiple domains with distinct distributions is not accessible due to some practical constraints. For example, federated learning [Truong et al, 2021] aggregates the information learned from a group of heterogeneous users. To preserve user privacy, the data of each user is stored locally, and only the trained models are transmitted to the central server.…”

Section: Introductionmentioning

confidence: 99%

On the Benefits of Selectivity in Pseudo-Labeling for Unsupervised Multi-Source-Free Domain Adaptation

Shen¹,

Bu²,

Wornell³

2022

Preprint

View full text Add to dashboard Cite

Due to privacy, storage, and other constraints, there is a growing need for unsupervised domain adaptation techniques in machine learning that do not require access to the data used to train a collection of source models. Existing methods for such multisource-free domain adaptation typically train a target model using supervised techniques in conjunction with pseudo-labels for the target data, which are produced by the available source models. However, we show that assigning pseudo-labels to only a subset of the target data leads to improved performance. In particular, we develop an informationtheoretic bound on the generalization error of the resulting target model that demonstrates an inherent bias-variance trade-off controlled by the subset choice. Guided by this analysis, we develop a method that partitions the target data into pseudolabeled and unlabeled subsets to balance the tradeoff. In addition to exploiting the pseudo-labeled subset, our algorithm further leverages the information in the unlabeled subset via a traditional unsupervised domain adaptation feature alignment procedure. Experiments on multiple benchmark datasets demonstrate the superior performance of the proposed method.

show abstract

Privacy preservation in federated learning: An insightful survey from the GDPR perspective

Cited by 180 publications

References 30 publications

HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection

HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection

QoS-Constrained Federated Learning Empowered by Intelligent Reflecting Surface

On the Benefits of Selectivity in Pseudo-Labeling for Unsupervised Multi-Source-Free Domain Adaptation

Contact Info

Product

Resources

About