Data Sharing Research Articles

A Unified Framework for Secure Healthcare Data Sharing: Integrating Federated Learning, Blockchain, and Quantum Cryptography

A Unified Framework for Secure Healthcare Data Sharing: Integrating Federated Learning, Blockchain, and Quantum Cryptography

My Holistic Data Share: A WEB3 Data Share Application: Extending Beyond Finance to Privacy-Protected Decentralized Share of Multi-Dimensional Data to Enhance Global Healthcare

Abstract WEB3.0 technologies on network architectures, distributed ledgers, and decentralized artificial intelligence represents a transformative shift in how data are handled, stored and shared. These innovations promise to significantly enhance consumer data privacy rights by addressing fundamental vulnerabilities associated with traditional centralized systems and self-custody wallets. Data breaches in traditional systems operated mainly by third parties are more common, resulting in significant data leaks because of centralized storage and excessive data movement, sometimes unnecessarily. Healthcare data breaches have been a growing concern globally. Several hospitals faced operational halts on account of the impact of ransomware on patient care and privacy. WEB3 Wallets are a vital component emerging as a significant force for global financial inclusion, especially in developing economies. They promote inclusion, reduce costs and empower individuals through self-custody. Though major improvements are needed in these wallets, their use is rising steadily. The global cryptocurrency user base is expected to reach over 500 million by 2025, with substantial growth in emerging markets, according to a report by Statista in 2023. This paper introduces a concept beyond cryptocurrencies and finance into everyday real-world use cases that need combinatorial access to a person’s holistic data, including financial and health records, genomic data, and advanced directives, among others, that need to be privacy protected and shared with specific actors identified for their roles in the WEB3 ecosystem through decentralized identifiers and non-fungible token badges identifying particular recipients. The author introduced the concept at ETHBoston in April 2024, won accolades for a primitive implementation using underlying threshold cryptography technologies, and enhanced it into a conceptual holistic data share application for global healthcare, as presented in this paper.

Identification of immunity- and ferroptosis-related signature genes as potential design targets for mRNA vaccines in AML patients.

Immune-associated ferroptosis plays an important role in the progression of acute myeloid leukemia (AML); however, the targets that play key roles in this process are currently unknown. This limits the development of mRNA vaccines based on immune-associated ferroptosis for clinical therapeutic applications. In this study, based on the rich data resources of the TCGA-LAML cohort, we analyzed the tumor mutational burden (TMB), gene mutation status, and associations between immune and ferroptosis genes to reveal the disease characteristics of AML patients. To gain a deeper understanding of differentially expressed genes, we applied the Limma package for differential expression analysis and integrated data sources such as ImmPort Shared Data and FerrDb V2. Moreover, we established gene modules related to TMB according to weighted gene coexpression network analysis (WGCNA) and explored the functions of these modules in AML and their relationships with TMB. We focused on the top 30 most frequent genes through a detailed survey of missense mutations and single nucleotide polymorphisms (SNPs) and selected potentially critical gene targets for subsequent analysis. Based on the expression of these genes, we successfully subgrouped AML patients and found that the subgroups associated with TMB (C1 and C2) exhibited significant differences in survival. The differences in the tumor microenvironment and immune cells between C1 and C2 patients were investigated with the ESTIMATE and MCP-counter algorithms. A predictive model of TMB-related genes (TMBRGs) was constructed, and the validity of the model was demonstrated by categorizing patients into high-risk and low-risk groups. The differences in survival between the high-risk patients and high-TMB patients were further investigated, and potential vaccine targets were identified via immune cell-level analysis. The identification of immunity- and ferroptosis-associated signature genes is an independent predictor of survival in AML patients and provides new information on immunotherapy for AML.

Public perspectives on increased data sharing in health research in the context of the 2023 National Institutes of Health Data Sharing Policy.

The National Institutes of Health (NIH) is the largest public research funder in the world. In an effort to make publicly funded data more accessible, the NIH established a new Data Management and Sharing (DMS) Policy effective January 2023. Though the new policy was available for public comment, the patient perspective and the potential unintended consequences of the policy on patients' willingness to participate in research have been underexplored. This study aimed to determine: (1) participant preferences about the types of data they are willing to share with external entities, and (2) participant perspectives regarding the updated 2023 NIH DMS policy. A cross-sectional, nationally representative online survey was conducted among 610 English-speaking US adults in March 2023 using Prolific. Overall, 50% of the sample identified as women, 13% as Black or African American, and 7% as Hispanic or Latino, with a mean age of 46 years. The majority of respondents (65%) agreed with the NIH policy, but racial differences were noted with a higher percentage (28%) of Black participants indicating a decrease in willingness to participate in research studies with the updated policy in place. Participants were more willing to share research data with healthcare providers, yet their preferences for data sharing varied depending on the type of data to be shared and the recipients. Participants were less willing to share sexual health and fertility data with health technology companies (41%) and public repositories (37%) compared to their healthcare providers (75%). The findings highlight the importance of adopting a transparent approach to data sharing that balances protecting patient autonomy with more open data sharing.

A double-masked, sham-controlled trial of rose bengal photodynamic therapy for the treatment of fungal and acanthamoeba keratitis: Rose Bengal Electromagnetic Activation with Green Light for Infection Reduction (REAGIR) study

BackgroundInfectious keratitis secondary to fungus or acanthamoeba often has a poor outcome despite receiving the best available medical therapy. In vitro rose bengal photodynamic therapy (RB-PDT) appears to be effective against fungal and acanthamoeba isolates (Atalay HT et al., Curr Eye Res 43:1322–5, 2018, Arboleda A et al. Am J Ophthalmol 158:64-70, 2014). In one published series, RB-PDT reduced the need for therapeutic penetrating keratoplasty in severe bacterial, fungal, and acanthamoeba keratitis not responsive to medical therapy.MethodsThis international, randomized, sham and placebo controlled 2-arm clinical trial randomizes patients with smear positive fungal and acanthamoeba and smear negative corneal ulcers in a 1:1 fashion to one of two treatment arms: 1) topical antimicrobial plus sham RB-PDT or 2) topical antimicrobial plus RB-PDT.DiscussionWe anticipate that RB-PDT will improve best spectacle-corrected visual acuity and also reduce complications such as corneal perforation and the need for therapeutic penetrating keratoplasty. This study will comply with the NIH Data Sharing Policy and Policy on the Dissemination of NIH-Funded Clinical Trial Information and the Clinical Trials Registration and Results Information Submission rule. Our results will be disseminated via ClinicalTrials.gov website, meetings, and journal publications. Our data will also be available upon reasonable request.Trial registrationNCT, NCT05110001, Registered on November 5, 2021.

Encouraging cycling through the improvement of streetscape perception: A bottom-up investigation into the relationship between street greening and bicycling volume

Enhancing neighborhood greenery quality proves to be an effective tactic in encouraging active travel among residents. From a cyclist's perspective, this study quantitatively evaluates street greening quality and its association with cycling behaviors. Utilizing shared bicycle usage data and SegNet-analyzed Baidu Street Views on Xiamen Island, we quantified comprehensive street greening quality. We then employed Spatial Design Network Analysis and a street greening quality rating scale to assess accessibility and aesthetics. Subsequently, models were developed using OLS, spatial autocorrelation, and Moran's I statistical methods, complemented by a spatial heterogeneity analysis of factors influencing shared bicycle riding behaviors through the Geographically Weighted Regression (GWR) model. Our study reveals that in arterial and commercial districts, the appeal and variety of street greening elements are more crucial than their quantity. These quality indicators exhibit a polycentric, continuous spatial distribution, aligning with the overall street layout. Beyond the visual impact of greenery on cycling behavior, factors like richness, openness, and safety of streets, along with diverse facilities, significantly influence cycling route preferences. In contrast, uniform street elements can disrupt or even discourage cycling. This research, by analyzing the relationship between street greening quality and cycling intensity, offers valuable insights for policymakers in prioritizing the transformation of streets into bike-friendly zones. Additionally, we propose a pragmatic, bottom-up approach to developing bicycle-friendly urban environments, indirectly fostering healthier travel habits among urban residents.

Towards accountable and privacy-preserving blockchain-based access control for data sharing

The integration of blockchain technology with Access Control (AC) systems presents novel opportunities for enhancing data security within decentralized architectures, which is drawing increasing attention in Data Sharing (DS) applications. However, existing works reveal a gap in achieving accountability for anonymous access in the absence of a centralized trusted authority. To address this issue, this paper introduces InvisiReveal, a novel Blockchain-Based AC (BBAC) framework that achieves permission invisibility, access anonymity, and accountability without extra trust assumptions. Users in InvisiReveal generate anonymous credentials to authenticate their requests using Zero Knowledge Proof. To enable accountability, a novel blockchain-oriented verifiable commitment (BC-VC) protocol is designed that allows a user to commit a confidential traceable tag to the blockchain. The system could unveil a malicious requester’s identity by opening the tag commitment under collaboration with the victim user and blockchain. We implement a prototype of InvisiReveal to evaluate its practicality, where an access request is verified within 5 ms.

Marketing Chain Analysis Of Big Red Chilli (Capsicum Annum L) In Kedawung District, Sragen Regency

This study investigates the marketing of large red chilies in Kedawung District, Sragen Regency, Central Java, where farmers often face fluctuating prices, forcing them to sell at the prevailing market rates. Conducted in December 2021, the research focuses on understanding the marketing channels for these chilies amidst unstable prices. Using a descriptive analytical method, the study involved 10 farmers, 10 marketing agencies, and 10 consumers to identify and analyze the marketing channels. The study reveals three main marketing channels with varying structures and margins. Channel I, directly linking farmers to consumers via collectors, has a marketing margin of IDR 2000/Kg and a farmer's share of 88.3%. Channel II, which includes retailers, has a margin of IDR 7000/Kg and a 69% farmer's share. Channel III, involving wholesalers and retailers, shows the highest margin of IDR 9000/Kg with a 62.5% farmer's share. Despite the price fluctuations, all channels are deemed efficient based on the farmer's share data.

Certificateless Proxy Re-encryption with Cryptographic Reverse Firewalls for Secure Cloud Data Sharing

Cloud computing has enabled data-sharing to be more convenient than ever before. However, data security is a major concern that prevents cloud computing from being widely adopted. A potential solution to secure data-sharing in cloud computing is proxy re-encryption (PRE), which allows a proxy to transform encrypted data from one key to another without accessing the plaintext. When using PRE, various challenges arise, including the leak of information by a trusted third party, collusion attacks, and issues associated with revocation. To overcome these challenges, this paper proposes a novel Certificateless Proxy Reencryption with Cryptographic Reverse Firewall for Secure Cloud Data Sharing (CLPRE-CRF). The new scheme enables secure distribution of encrypted data from a data owner to users through public clouds. Meanwhile, the CLPRE-CRF scheme can resist exfiltration of secret information and forgery of ciphertext in case the scheme is compromised. In addition, the scheme provides a flexible revocation mechanism to prevent unauthorized access to private data. The security analysis demonstrates that the CLPRE-CRF resists chosen-plaintext attacks and collusion attacks. Moreover, performance evaluation indicates that our scheme achieves a 14% and 22% reduction in computation costs during the encryption and decryption algorithms, respectively. Therefore, the proposed CLPRE-CRF scheme is well-suited for cloud computing environments.

Use of Standardized Nursing Terminologies to Capture Social Determinants of Health Data: An Integrative Review.

Addressing social determinants of health in nursing care is important for improving health outcomes and reducing health inequities. Using standardized nursing terminologies to capture this information generates sharable data that can be used to achieve these goals and create new knowledge. The purpose of this integrative review was to examine use of standardized nursing terminologies for collecting social determinants of health data in nursing research and practice. The CINAHL, MEDLINE, and Web of Science databases were searched using the terms "social determinants of health" [and] "nursing" [and] "standardized terminology" or names for each of the 12 American Nurses Association-approved terminologies. Limiters included peer-reviewed and English language. After removal of duplicates, 120 articles were found and screened for relevance and quality using a three-step process. This yielded a final sample of seven articles. Article data were extracted and analyzed for themes. In all articles, retrospective, observational, or secondary analysis research designs were used to analyze previously collected data from large, deidentified datasets or research studies. The Omaha System was the only standardized nursing terminology represented in the sample. All operational definitions of social determinants of health included behavioral items. In most studies, a social determinants of health index score was calculated, and data were analyzed using descriptive statistics and visualization methods. Results reported across the articles were diverse; some themes were identified. This review revealed published literature on this topic is limited. More quality improvement and multisite studies that examine the use of standardized nursing terminologies by nurses to collect and use social determinants of health data are needed.

SODA: Software to Support the Curation and Sharing of FAIR Autonomic Nervous System Data

SODA: Software to Support the Curation and Sharing of FAIR Autonomic Nervous System Data

Assessment of loss of life owing to dam-failure flooding considering population distribution and evacuation

Dam failures pose a serious threat to the safety of downstream populations. Scientifically assessing loss of life (LOL) from dam-failure flooding contributes to the emergency response. Population distribution varies spatially and temporally even within a day, and has different information dissemination and response speeds, which impacts the chances of safe evacuation. This study established an LOL assessment model that considers the initial population distribution and population evacuation process. Shared bike big data and other multi-source data were used to identify daily population activity. The evacuation process includes receiving warnings, responses, and horizontal or vertical evacuation choices, and the evacuation results were then evaluated. This model was applied to a dam in China. During nighttime, commuting, and working hours, the issuance of warnings 214 min, 137 min, and 0 min prior to dam failure, respectively, significantly mitigated LOL. Under the same communication and response speeds, the commuting period poses the highest risk of LOL. Assessments of LOL due to dam failure flooding need to consider the impact of the timing of dam failure and population distribution. The model used in this study proposes varying effective early warning times based on the time of dam failure occurrence, which can contribute to effective dam risk management, helping prevent personnel loss.

Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology

In the current landscape of medical data management, processing data across diverse institutions and maximizing their value are paramount. However, traditional methods lack a secure and efficient mechanism for end-to-end traceability and supervision, posing challenges in distributed scenarios lacking mutual trust. Leveraging blockchain’s decentralized, tamper-proof, and traceable features, this paper introduces a blockchain-based medical data management platform. This platform enables full-process management of raw data, operational behaviors, intermediate data, and final data, meeting the needs of trusted storage and supervision of data. We propose two methods, namely, naive method and DAG-based method, to realize forward tracking and backward tracing of medical data stored on the blockchain, respectively. We validated and analyzed the storage and query performance of the medical data management platform on real medical data, and we also conducted experimental analyses on the efficiency of the proposed traceability algorithm under different data scales and processing path lengths. The results demonstrate that our platform and traceability methods effectively meet the management needs of medical data distributed across institutions.

Blockchain-Based Data Security and Sharing for Resource-Constrained Devices in Manufacturing IoT

Blockchain-Based Data Security and Sharing for Resource-Constrained Devices in Manufacturing IoT

Competition or Diversion? Effect of Public Sharing of Data on Research Productivity of Data Provider

Competition or Diversion? Effect of Public Sharing of Data on Research Productivity of Data Provider

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

Paths to Data Transfer: Exploring Routine Change for Compliant Data Sharing

Paths to Data Transfer: Exploring Routine Change for Compliant Data Sharing

Trusted access control mechanism for data with blockchain-assisted attribute encryption

In the growing demand for data sharing, how to realize fine-grained trusted access control of shared data and protect data security has become a difficult problem. Ciphertext policy attribute-based encryption (CP-ABE) model is widely used in cloud data sharing scenarios, but there are problems such as privacy leakage of access policy, irrevocability of user or attribute, key escrow, and trust bottleneck. Therefore, we propose a blockchain-assisted CP-ABE (B-CP-ABE) mechanism for trusted data access control. Firstly, we construct a data trusted access control architecture based on the B-CP-ABE, which realizes the automated execution of access policies through smart contracts and guarantees the trusted access process through blockchain. Then, we define the B-CP-ABE scheme, which has the functions of policy partial hidden, attribute revocation, and anti-key escrow. The B-CP-ABE scheme utilizes Bloom filter to hide the mapping relationship of sensitive attributes in the access structure, realizes flexible revocation and recovery of users and attributes by re-encryption algorithm, and solves the key escrow problem by joint authorization of data owners and attribute authority. Finally, we demonstrate the usability of the B-CP-ABE scheme by performing security analysis and performance analysis.

Inherit or innovate?–evidence from China’s auto industry’s new car series naming

The automotive industry introduces a sizable number of new automobile products annually. The naming of new products may have significant impact on their market performance. Diverging from prior research on the impact of product names, companies’ innovative consciousness endows them with a certain level of proactive positioning, thereby implementing independent naming strategies after considering product factors and consumer preferences passively. This paper analyzes the drivers of the market performance of sequential products on the naming of new products in Chinese automotive industry. We collected market share data and names of various car series from January 2007 to September 2022. Then, we calculated semantic distances between names and performed empirical tests. Our findings show that firms demonstrate a risk preference pattern contrary to traditional prospect theory when using the previous car series’ market share as a reference point. In the interplay between market orientation and risk preferences, the relationship between the market share of the last car series and the similarity in names between old and new car series follows an inverted U-shaped pattern. This study extends the discussion on risk preferences within the behavioral theory of the firm (BTOF) and proposes a decision mechanism for product naming that considers market and firm factors during the response phase of market orientation.

Spatiotemporal analysis of sand and dust emission point sources detected from satellite imagery in Syria, Jordan, and Iraq

This study utilized MODIS true color satellite imagery to analyse blowing sand and dust events dynamics in the Middle East from 2010 to 2021, focusing on Syria, Iraq, and Jordan. A total of 4923 dust point sources were detected, with a significant concentration (~90 %) located within the Tigris-Euphrates Basin (Nearest Neighbor Ratio = 0.41, р < 0.001). Land cover analysis revealed that bare land, comprising most of the study area, was the predominant source of dust emissions. Wetlands, though only constituting about 1 % of the area, showed the highest frequency of dust sources per unit area, highlighting their role as critical dust emission hotspots. The study emphasizes the impact of drought and anthropogenic factors, such as poor land management, on blowing dust intensity. It suggests the necessity of strategic land management practices, including re-vegetation of arid areas, reducing soil exposure, and implementing wind erosion control measures. To effectively address the transboundary nature of dust emissions, the findings underscore the importance of fostering regional cooperation through mechanisms such as shared environmental monitoring and data exchange platforms, joint management of cross-border natural resources, and collaborative policy making.