The outbreak of COVID-19 has brought great pain to people around the world. As an epidemic prevention and control measure, the health QR code (HC) has been designed to trace the confirmed cases and ...close contacts quickly. Although some existing health code schemes preserve the privacy, but most of them are either unsupported for fine-grained auditability or centralised health code storage. Therefore, we propose a multi-dimension traceable privacy-preserving HC scheme based on blockchain. It prevents health code information being tampered with and supports the traceability of virus transmission chain. We utilise attribute-based encryption to protect residents' privacy information and achieve fine-grained access control. Furthermore, to support the multi-dimension traceability by the epidemic prevention and control departments, the searchable encryption has been introduced. Finally, we give the security analysis and performance evaluation to verify the feasibility and practical significance of our scheme.
Abstract
Implementing effective regulation of blockchain transactions has become a research hotspot in recent years. However, most of the current regulatory schemes are customized for specific ...blockchain applications and lack versatility and scalability. Meanwhile, these schemes cannot guarantee fairness due to the non‐disclosure of regulatory policies and regulatory processes. To address these issues, a scalable policy‐based regulatory architecture (SPRA) is proposed for blockchain transactions that separates regulation and application to provide sufficient scalability. SPRA is a four‐layer model (permission layer, regulation layer, bridge layer, and business layer). A regulatory policy description language (XRPL) is designed to define the regulatory rules and specifications for interoperability between the layers. A decentralized jury mechanism (JuryBC) based on the Shamir threshold secret sharing algorithm and Pedersen commitment is proposed at the regulation layer to avoid regulatory arbitrariness and unfairness. We also construct a secure and efficient regulatory data sharing scheme (RDShare) at the business layer using an attribute‐based encryption algorithm. The key parameters in both JuryBC and RDShare can be specified in the regulatory policy to suit different application scenarios. Finally, the security of the architecture is analyzed and the feasibility and scalability of the architecture through simulation experiments are demonstrated.
Abstract Logistics supply chain (LSC), a chain structure that integrates and coordinates all logistics transactions, has become an essential component of the modern logistics industry. By using ...blockchain, trusted logistics services enable participants to effectively record and track transactions during the logistics process. Current blockchain‐based LSC features distributed structure and data privacy requirements, hindering the supervision of logistics transactions. Leveraging emerging dual‐blockchain architecture to separate logistics transactions from supervision is a promising direction. However, the dual‐blockchain collaboration restricts supervision due to its cross‐chain privacy and efficiency. To address these issues, a logistics supply chain supervision scheme based on dual‐blockchain collaboration (DBC) is proposed. First, an independent supervision blockchain is constructed to balance the contradiction between distributed structure and supervision requirements. Second, two mechanisms are designed to enhance the privacy and performance of collaborative supervision. The hybrid access control mechanism enables fine‐grained supervision for different participants, and the aggregated transaction verification method supports efficient collaboration for logistics transactions. Security analysis and performance evaluation demonstrate the feasibility of DBC in enhancing the security and supervision of logistics data on the dual‐blockchain architecture. Experimental results show that the cross‐chain supervision overhead of DBC is reduced to of the baseline schemes.
Electronic auctions (e-auctions) remove the physical limitations of traditional auctions and bring this mechanism to the general public. However, most e-auction schemes involve a trusted auctioneer, ...which is not always credible in practice. Some studies have applied cryptography tools to solve this problem by distributing trust, but they ignore the existence of collusion. In this paper, a blockchain-based Privacy-Preserving and Collusion-Resistant scheme (PPCR) for double auctions is proposed by employing both cryptography and blockchain technology, which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy. A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance. A Dispute Resolution agreement (DR) is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct. In addition, a Concise Dispute Resolution protocol (CDR) is designed to handle situations where the number of accused winners is small, significantly reducing the computation cost of dispute resolution. Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.
The logistics information management system has been widely used, due to its high internal operation efficiency, the visualization of package transportation, and the real-time sharing of information ...between logistics companies and customers. However, personal and logistics privacy preservation is still an issue that needs to be resolved in the logistics process. In addition, the opacity and non-traceability of logistics information reduce the security and availability of the system. We propose a blockchain-based logistics privacy-preserving scheme to ensure that personal and logistics information is auditable and traceable. We implemented the prototype system with the Hyperledger Fabric platform and verified the scheme's feasibility through performance evaluation.
Background Acute hematologic toxicity (HT) is a prevalent adverse tissue reaction observed in cervical cancer patients undergoing chemoradiotherapy (CRT), which may lead to various negative effects ...such as compromised therapeutic efficacy and prolonged treatment duration. Accurate prediction of HT occurrence prior to CRT remains challenging. Methods A discovery dataset comprising 478 continuous cervical cancer patients (140 HT patients) and a validation dataset consisting of 205 patients (52 HT patients) were retrospectively enrolled. Both datasets were categorized into the CRT group and radiotherapy (RT)-alone group based on the treatment regimen, i.e., whether chemotherapy was administered within the focused RT duration. Radiomics features were derived by contouring three regions of interest (ROIs)—bone marrow (BM), femoral head (FH), and clinical target volume (CTV)—on the treatment planning CT images before RT. A comprehensive model combining the radiomics features as well as the demographic, clinical, and dosimetric features was constructed to classify patients exhibiting acute HT symptoms in the CRT group, RT group, and combination group. Furthermore, the time-to-event analysis of the discriminative ROI was performed on all patients with acute HT to understand the HT temporal progression. Results Among three ROIs, BM exhibited the best performance in classifying acute HT, which was verified across all patient groups in both discovery and validation datasets. Among different patient groups in the discovery dataset, acute HT was more precisely predicted in the CRT group area under the curve (AUC) = 0.779, 95% CI: 0.657–0.874 than that in the RT-alone (AUC = 0.686, 95% CI: 0.529–0.817) or combination group (AUC = 0.748, 95% CI: 0.655–0.827). The predictive results in the validation dataset similarly coincided with those in the discovery dataset: CRT group (AUC = 0.802, 95% CI: 0.669–0.914), RT-alone group (AUC = 0.737, 95% CI: 0.612–0.862), and combination group (AUC = 0.793, 95% CI: 0.713–0.874). In addition, distinct feature sets were adopted for different patient groups. Moreover, the predicted HT risk of BM was also indicative of the HT temporal progression. Conclusions HT prediction in cervical patients is dependent on both the treatment regimen and ROI selection, and BM is closely related to the occurrence and progression of HT, especially for CRT patients.
Abstract
Resting-state functional connectivity (RSFC) has been widely adopted for individualized trait prediction. However, multiple confounding factors may impact the predicted brain-behavior ...relationships. In this study, we investigated the impact of 4 confounding factors including time series length, functional connectivity (FC) type, brain parcellation choice, and variance of the predicted target. The data from Human Connectome Project including 1,206 healthy subjects were employed, with 3 cognitive traits including fluid intelligence, working memory, and picture vocabulary ability as the prediction targets. We compared the prediction performance under different settings of these 4 factors using partial least square regression. Results demonstrated appropriate time series length (300 time points) and brain parcellation (independent component analysis, ICA100/200) can achieve better prediction performance without too much time consumption. FC calculated by Pearson, Spearman, and Partial correlation achieves higher accuracy and lower time cost than mutual information and coherence. Cognitive traits with larger variance among subjects can be better predicted due to the well elaboration of individual variability. In addition, the beneficial effects of increasing scan duration to prediction partially arise from the improved test–retest reliability of RSFC. Taken together, the study highlights the importance of determining these factors in RSFC-based prediction, which can facilitate standardization of RSFC-based prediction pipelines going forward.
