PVD-FL: A Privacy-Preserving and Verifiable Decentralized Federated Learning Framework

Zhao, Jiaqi; Zhu, Hui; Wang, Fengwei; Lu, Rongxing; Liu, Zhe; Li, Hui

doi:10.1109/tifs.2022.3176191

Cited by 54 publications

(16 citation statements)

References 36 publications

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“…Additionally, researchers should explore adaptive and context-aware mechanisms that consider the unique characteristics of various application scenarios and adapt the learning process accordingly. A common limitation in secure and verifiable FL frameworks [127], [128], [129], [130], [131], [132], [133] is the reliance on trusted third parties for authentication and aggregation. This dependence could introduce security vulnerabilities and single points of failure.…”

Section: Discussionmentioning

confidence: 99%

“…2) Homomorphic Encryption and Hash Function based Varifiability: Authors in [129], introduces PVD-FL, a secure, verifiable, and privacy-centric decentralized FL structure aimed at mitigating security threats such as privacy invasion and integrity breaches. The referenced work in [130] presents VerifyNet, a groundbreaking framework that addresses verifiability and privacy concerns in FL.…”

Section: Security and Privacy Aware Trustworthy Flmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Security and Privacy Aware Trustworthy Flmentioning

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

View full text Add to dashboard Cite

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“…Since these schemes [15,16] require interaction among participants to protect privacy, the communication overhead is high. Zhao et al [17] proposed a decentralized scheme. However, this scheme requires participants to perform in sequence.…”

Section: Related Workmentioning

confidence: 99%

SVFL: A secure and verifiable federated learning scheme

Wang

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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Federated learning can effectively alleviate the data privacy problem of the participants, but the parameters or gradients passed in the model training may still leak the private data of the participants. Worse, aggregation server may return fake aggregation results. Existing solutions either use complex cryptographic primitives such as zero-knowledge proofs, or require interaction among participants, causing them high computation or communication overhead. Therefore, this paper proposes a secure and verifiable federated learning (SVFL) scheme. Specifically, SVFL performs privacy protection by introducing noise that can be offset during the aggregation process, and utilizes linear homomorphic hash to verify the correctness of the aggregation results. Compared with existing schemes, SVFL hardly loses accuracy due to the introduced security mechanism, and has low computation and communication overhead. Experimental results show that the performance of SVFL is almost consistent with the original federated learning without any protection, which makes SVFL applicable to edge devices. The computation and communication overhead of SVFL does not increase with the number of participants, which makes SVFL applicable to high-concurrency scenarios.

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“…This makes them unsuitable for practical applications that require both privacy and performance. Moreover, optimizing differential privacy parameters remains a challenge in dynamic UAV-MEC environments characterized by resource constraints and potential data collection attacks [ 25 ]. This limited scope results in incomplete solutions that fail to address real systemic problems or lack versatility.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Privacy Protection Mechanism for Blockchain-Based Federated Learning System in UAV-MEC Networks

Zhu,

Qin

2024

Sensors

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The widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to the transmission of sensitive data. Traditional UAV-MEC systems with centralized data processing expose this data to risks like breaches and manipulation, potentially hindering the adoption of these valuable technologies. To address this critical challenge, we propose UBFL, a novel privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy (DP), UBFL employs an adaptive nonlinear encryption function to safeguard the privacy of UAV model updates while maintaining data integrity and accuracy. This innovative approach enables rapid convergence, allowing the base station to efficiently identify and filter out severely compromised UAVs attempting to inject malicious data. Additionally, UBFL incorporates the Random Cut Forest (RCF) anomaly detection algorithm to actively identify and mitigate poisoning data attacks. Extensive comparative experiments on benchmark datasets CIFAR10 and Mnist demonstrably showcase UBFL’s effectiveness. Compared to DP-based methods, UBFL achieves accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) in privacy preservation while maintaining superior accuracy. Notably, under data pollution scenarios with varying attack sample rates (10%, 20%, and 30%), UBFL exhibits exceptional resilience, highlighting its robust capabilities in securing UAV gradients within MEC environments.

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PVD-FL: A Privacy-Preserving and Verifiable Decentralized Federated Learning Framework

Cited by 54 publications

References 36 publications

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

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

SVFL: A secure and verifiable federated learning scheme

An Efficient Privacy Protection Mechanism for Blockchain-Based Federated Learning System in UAV-MEC Networks

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