A Blockchain-Empowered Federated Learning in Healthcare-Based Cyber Physical Systems

Liu, Yuan; Yu, Wangyuan; Ai, Zhengpeng; Xu, Guangxia; Zhao, Liang; Tian, Zhihong

doi:10.1109/tnse.2022.3168025

Cited by 47 publications

(13 citation statements)

References 37 publications

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“…It is evident that collecting abundant and reliable data often requires expensive clinician examination or confirmatory molecular tests as gold standard. Collecting such a data at scale and feeding it into machine learning models may raise patient data privacy concerns and entail data linkage considerations 57 . Moreover, the data may just not be readily available for rare conditions.…”

Section: Discussionmentioning

confidence: 99%

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening

Xiong

Berkovsky

Kâafar

et al. 2022

Sci Rep

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Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine learning based alternatives to PCR screening for SARS-CoV-2 have been proposed, including cough sound analysis. Cough classification models appear to be a robust means to predict infective status, but collecting reliable PCR confirmed data for their development is challenging and recent work using unverified crowdsourced data is seen as a viable alternative. In this study, we report experiments that assess cough classification models trained (i) using data from PCR-confirmed COVID subjects and (ii) using data of individuals self-reporting their infective status. We compare performance using PCR-confirmed data. Models trained on PCR-confirmed data perform better than those trained on patient-reported data. Models using PCR-confirmed data also exploit more stable predictive features and converge faster. Crowd-sourced cough data is less reliable than PCR-confirmed data for developing predictive models for COVID-19, and raises concerns about the utility of patient reported outcome data in developing other clinical predictive models when better gold-standard data are available.

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Section: Discussionmentioning

confidence: 99%

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening

Xiong

Berkovsky

Kâafar

et al. 2022

Sci Rep

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“…It can successfully defend against switching attacks (CCS 2007, Boldyreva et al [8]) and reordering attacks (ISPEC 2007, Shao [9]). In recent years, the combination of signature scheme and blockchain technology [10][11][12], federated learning technology [13], 6G network [14], homomorphic learning [15], network routing protocol [16], edge computing [17], vehicular ad hoc networks [18], and software-defned vehicular network [19][20][21] by applying signature algorithms and encryption algorithms to the experimental scheme to further strengthen the security of the scheme and improve the privacy protection capability of the scheme is also a hot topic. In the blockchain, the digital signature is one of the three basic technologies, and its importance is self-evident.…”

Section: Related Workmentioning

confidence: 99%

New Constructions of Existential Unforgeable Aggregate Signature Scheme from CSP

Zou

Huang

et al. 2022

Security and Communication Networks

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In future, hundreds of years of mathematical problems that the security of public key cryptography algorithms rely on may be defeated by quantum algorithms. How can a digital signature scheme gracefully balance security and efficiency? This study uses the conjugate search problem and the left self-distributive system to combine and uses the RSA-like algorithm as the underlying structure to propose a new aggregated signature scheme. We, through the EUF game, under the random metaphor model, prove that the security of the scheme satisfies the adaptation unforgeability under selective message attack, the scheme can be finally reduced to the discrete logarithm problem or large prime number decomposition problem. In addition, we can achieve antiquantum attack and exhaustive attack by performing matrix calculations on the message, defining and changing the structure of the matrix by encoding, and setting thresholds for the matrix dimension and the length of the private key. In terms of efficiency, the message signature implementation is linear compared with the expansion rate in terms of storage and computing overhead, and the generation and verification of the final signature pair have nothing to do with the number of users. In addition, the length of the signature is fixed and the size is only the length of a single group, which effectively reduces the generation of public and private key pairs and saves a lot of storage space. The storage space and computational complexity are also effectively improved compared with other solutions.

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“…Among the expectations of CBDCs, there is the possibility of having cash-like features [ 13 ], such as offline payments. The reason behind the requirement for cash-like features is anonymity—a fundamental human right [ 14 ] that not even blockchains can ensure, despite some very interesting works that exist in the literature [ 15 , 16 , 17 ]. We are always connected to the internet and we leave digital traces everywhere.…”

Section: Introductionmentioning

confidence: 99%

A Sustainable Approach to Delivering Programmable Peer-to-Peer Offline Payments

Mainetti

Aprile

Mele

et al. 2023

Sensors

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Payment apps and digital wallets are powerful tools used to exchange e-money via the internet. However, with the progressive disappearance of cash, there is a need for the digital equivalent of physical banknotes to guarantee the same level of anonymity of private payments. Few efforts to solve the double-spending problem exist in P2P payments (i.e., in avoiding the possibility of a payer retaining copies of digital coins in absence of a trusted third party (TTP)), and further research efforts are needed to explore options to preserve the privacy of payments, as per the mandates of numerous central bank digital currency (CBDC) exploratory initiatives, such as the digital euro. Moreover, generic programmability requirements and energetic impacts should be considered. In this paper, we present a sustainable offline P2P payment scheme to face the double-spending problem by means of a one-time program (OTP) approach. The approach consists of wiping the business logic out of a client’s app and allowing financial intermediaries to inject a certified payment code into the user’s device, which will execute (asynchronously and offline) at the time of payment. To do so, we wrap each coin in a program at the time of withdrawal. Then the program exploits the trusted execution environment (TEE) of modern smartphones to transfer itself from the payer to the payee via a direct IoT link. To confirm the validity of the approach, we performed qualitative and quantitative evaluations, specifically focusing on the energetic sustainability of the proposed scheme. Results show that our payment scheme is energetically sustainable as the current absorbed for sending one coin is, at most, ~1.8 mAh on an Apple smartphone. We advance the state-of-the-art because the scheme meets the programmability, anonymity, and sustainability requirements (at the same time).

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A Blockchain-Empowered Federated Learning in Healthcare-Based Cyber Physical Systems

Cited by 47 publications

References 37 publications

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening

New Constructions of Existential Unforgeable Aggregate Signature Scheme from CSP

A Sustainable Approach to Delivering Programmable Peer-to-Peer Offline Payments

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