Privacy-Preserving Aggregation in Federated Learning: A Survey

Liu, Ziyao; Guo, Jiale; Yang, Weiming; Fan, Jiani; Lam, Kwok-Yan; Zhao, Jun

doi:10.1109/tbdata.2022.3190835

Cited by 44 publications

(21 citation statements)

References 206 publications

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“…These mechanisms provide security to the communicated data. In FL systems, privacy issues are often resolved via cryptographic techniques, perturbation techniques, and anonymization techniques [61].…”

Section: Based On the Privacy Mechanismmentioning

confidence: 99%

Federated Learning Based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks

et al. 2023

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Our daily lives are significantly impacted by intelligent Internet of Things (IoT) application, services, IoT gadgets, and more intelligent industries. Artificial Intelligence (AI) is anticipated to have a substantial impact on training machine learning algorithms on IoT devices without sharing data. As data privacy has become a serious societal concern, Federated Learning (FL) has emerged as a hot research area for enabling the collaborative training of machine learning models across many smart IoT devices while adhering to privacy constraints. Although, FL is utilized for preserving privacy in IoT networks, but it is also facing some challenges such as privacy, effectiveness, and efficiency. In this article, a taxonomy of FL-based IoT systems is proposed and an analysis of related works on FL-based IoT systems is presented. Further, a comprehensive study of various types of threats, attacks, and frameworks is done. In addition to this, a taxonomy on privacy-preserving FL techniques for IoT networks is also devised. Finally, the study is concluded by highlighting the various open research challenges in FL-based IoT networks.

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Section: Based On the Privacy Mechanismmentioning

confidence: 99%

Federated Learning Based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks

et al. 2023

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“…Recently, an extensive survey of existing attempts to construct privacypreserving FL protocols [18] was published. Of the investigated works, the most promising for achieving an incentive-compatible decentralized protocol is [24].…”

Section: Marvel DCmentioning

confidence: 99%

Marvel DC: A Blockchain-Based Decentralized and Incentive-Compatible Distributed Computing Protocol

McMenamin¹,

Daza²

2022

Preprint

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Decentralized computation outsourcing should allow anyone to access the large amounts of computational power that exists in the Internet of Things.Unfortunately, when trusted third parties are removed to achieve this decentralization, ensuring an outsourced computation is performed correctly remains a significant challenge. In this paper, we provide a solution to this problem.We outline Marvel DC, a fully decentralized blockchain-based distributedcomputing protocol which formally guarantees that computers are strictly incentivized to correctly perform requested computations. Furthermore, Marvel DC utilizes a reputation management protocol to ensure that, for any minority of computers not performing calculations correctly, these computers are identified and selected for computations with diminishing probability. We then outline Privacy Marvel DC, a privacy-enhanced version of Marvel DC which decouples results from the computers which computed them, making the protocol suitable for computations such as Federated Learning, where results can reveal sensitive information about that computer that computed them. We provide an implementation of Marvel DC and analyses of both protocols, demonstrating that This document is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 814284

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“…Fairness-aware FL is examined in [22], where client selection, incentive mechanisms, and contribution evaluations are explored with a taxonomy, but the paper does not focus on all aspects of trustworthiness in FL. Research works in [23], [24], [25], [26] discuss the security and privacy aspects in FL, exploring cryptographic techniques, secure multi-party computation, differential privacy, secure data aggregation, trust management, and secure model aggregation. These papers are not focusing on trust factor.…”

Section: Literature Reviewmentioning

confidence: 99%

Common misconceptions regarding COVID-19 among health care professionals: an online global cross-sectional survey

Tariq¹,

Hamid²,

Mashood³

et al. 2020

J Oral Res

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Background: The COVID-19 pandemic has taken the world by storm, with cases continuously increasing by the hour and with a shortage of information regarding the virus available to the general public. Despite the availability of trusted online sources, there are still misconceptions relating to the virus floating around. This study aimed to find out the level of misconceptions among healthcare professionals (HCPs) worldwide about the ensuing COVID-19 pandemic. Materials and Methods: A descriptive cross sectional study was conducted through a global online survey. The self-administered questionnaire was designed and registered at an online website (Kwiksurveys). A total of 652 participants from 35 different countries across the world responded. Statistical analysis was performed using SPSS version 23. Socio-demographic and discipline characteristics were compared with the help of the chi-square test and univariate and multivariate logistic regression to find significant relationships. Results: Among all HCPs, general physicians (61.2%) were the most common respondents. The responses from females (63.3%) almost doubled those of men, and nearly half of the participants were working in private institutes (49.2%). Additionally, the major source of information used by HCPs about COVID-19 was social media (55.4%). Regarding misconception assessment, 71.6% of participants had correct concepts regarding COVID-19. However, 28.4% had incorrect information. Female HCPs were 1.49 times more likely to have correct concepts compared to males (OR=1.49, 95% CI=1.04-2.14). Conclusions: The majority of the HCPs were keeping themselves up-to-date with current information concerning the knowledge, prevention, and hygiene practices of COVID-19 infection. However, some misconceptions are deeply rooted in the mindsets of HCPs worldwide and need to be addressed by the continuous professional development of HCPs. The availability of reliable sources of information on the pandemic should be encouraged, with adequate explanations also available to the general public in simple terms.

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Privacy-Preserving Aggregation in Federated Learning: A Survey

Cited by 44 publications

References 206 publications

Federated Learning Based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks

Federated Learning Based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks

Marvel DC: A Blockchain-Based Decentralized and Incentive-Compatible Distributed Computing Protocol

Common misconceptions regarding COVID-19 among health care professionals: an online global cross-sectional survey

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