Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy a...Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.展开更多
With the rapid development of web3.0 applications,the volume of data sharing is increasing,the inefficiency of big data file sharing and the problem of data privacy leakage are becoming more and more prominent,and the...With the rapid development of web3.0 applications,the volume of data sharing is increasing,the inefficiency of big data file sharing and the problem of data privacy leakage are becoming more and more prominent,and the existing data sharing schemes have been difficult to meet the growing demand for data sharing,this paper aims at exploring a secure,efficient and privacy-protecting data sharing scheme under web3.0 applications.Specifically,this paper adopts interplanetary file system(IPFS)technology to realize the storage of large data files to solve the problem of blockchain storage capacity limitation,and utilizes ciphertext policy attribute-based encryption(CP-ABE)and proxy re-encryption(PRE)technology to realize secure multi-party sharing and finegrained access control of data.This paper provides the detailed algorithm design and implementation of data sharing phases and processes,and analyzes the algorithms from the perspectives of security,privacy protection,and performance.展开更多
The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facili...The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.展开更多
With the rapid development of medical data sharing,issues of privacy and ownership have become prominent,which have limited the scale of data sharing.To address the above challenges,we propose a blockchainbased data-s...With the rapid development of medical data sharing,issues of privacy and ownership have become prominent,which have limited the scale of data sharing.To address the above challenges,we propose a blockchainbased data-sharing framework to ensure data security and encourage data owners to actively participate in sharing.We introduce a reliable attribute-based searchable encryption scheme that enables fine-grained access control of encrypted data and ensures secure and efficient data sharing.The revenue distribution model is constructed based on Shapley value to motivate participants.Additionally,by integrating the smart contract technology of blockchain,the search operation and incentive mechanism are automatically executed.Through revenue distribution analysis,the incentive effect and rationality of the proposed scheme are verified.Performance evaluation shows that,compared with traditional data-sharing models,our proposed framework not only meets data security requirements but also incentivizes more participants to actively participate in data sharing.展开更多
The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,inclu...The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,including enterprises,agents,and government departments.However,the urgent issue that requires immediate attention is how to achieve secure and efficient cross-border data sharing among these government departments and enterprises in complex trade processes.In addressing this need,this paper proposes a data exchange architecture employing Multi-Authority Attribute-Based Encryption(MA-ABE)in combination with blockchain technology.This scheme supports proxy decryption,attribute revocation,and policy update,while allowing each participating entity to manage their keys autonomously,ensuring system security and enhancing trust among participants.In order to enhance system decentralization,a mechanism has been designed in the architecture where multiple institutions interact with smart contracts and jointly participate in the generation of public parameters.Integration with the multi-party process execution engine Caterpillar has been shown to boost the transparency of cross-border information flow and cooperation between different organizations.The scheme ensures the auditability of data access control information and the visualization of on-chain data sharing.The MA-ABE scheme is statically secure under the q-Decisional Parallel Bilinear Diffie-Hellman Exponent(q-DPBDHE2)assumption in the random oracle model,and can resist ciphertext rollback attacks to achieve true backward and forward security.Theoretical analysis and experimental results demonstrate the appropriateness of the scheme for cross-border data collaboration between different institutions.展开更多
Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data ...Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data sharing mechanisms.Design/methodology/approach:By integrating the UTAUT model,trust theory and self-efficacy theory,introducing the concepts of data transparency and individual innovation,and combining internal and external motivators,we constructed a conceptual model of medical data users’sharing behavior in clinical research scenarios.We conducted empirical research by collecting 360 pieces of first-hand data from clinical researchers.Findings:Among the internal motivators,effort expectation had a higher impact on sharing intention than performance expectation,individual innovation and self-efficacy had a higher impact on sharing behavior than trust.Trust does not show a significant impact on sharing intention,but it has a significant positive influence on sharing behavior.Among the external motivators,community influence and data transparency both positively affect sharing intention.In addition,users with different working years,professional status,data level needs,and different sharing experiences showed significant differences in healthcare data sharing.Research limitations:Our sample of clinical researchers from China was used as empirical data.Further research is needed to examine the generality of the study findings.Practical implications:The findings enhance healthcare data stakeholders’understanding of healthcare data sharing in clinical research scenarios and provide theoretical and practical insights for relevant researchers.Originality/value:In this study,the UTAUT model,trust theory and self-efficacy theory were integrated and applied to clinical research scenarios for the first time,and the concepts of data transparency and individual innovation were introduced,and the CRS-USB conceptual model was constructed and validated to extend the UTAUT model.展开更多
In the process of implementing data openness between banks and fin-tech companies,as the breadth and depth of cooperation between banks and enterprises continue to increase,there is a risk of“too much correlation to ...In the process of implementing data openness between banks and fin-tech companies,as the breadth and depth of cooperation between banks and enterprises continue to increase,there is a risk of“too much correlation to fail”and“too many links to fail”.There are problems with the implementation of financial data openness by regulatory agencies for banks and fin-tech enterprises,such as the ambiguity of regulatory responsibilities,the emphasis on financial regulatory goals,and the lag in regulatory methods.To address these issues,it is necessary to clarify the responsibilities of financial regulatory agencies,establish a collaborative mechanism for financial regulation,coordinate the types of risks in bank enterprise cooperation,achieve the technical implementation of financial regulatory measures and the design of regulatory systems,obtain regulatory data in real time,establish a hierarchical regulatory system for bank enterprise cooperation to improve the regulatory path,and ensure the rational and legal use of financial data in bank enterprise cooperation.展开更多
The growing demand for international travel has highlighted the critical need for reliable tools to verify travelers’healthcare status and meet entry requirements.Personal health passports,while essential,face signif...The growing demand for international travel has highlighted the critical need for reliable tools to verify travelers’healthcare status and meet entry requirements.Personal health passports,while essential,face significant challenges related to data silos,privacy protection,and forgery risks in global sharing.To address these issues,this study proposes a blockchain-based solution designed for the secure storage,sharing,and verification of personal health passports.This innovative approach combines on-chain and off-chain storage,leveraging searchable encryption to enhance data security and optimize blockchain storage efficiency.By reducing the storage burden on the blockchain,the system ensures both the secure handling and reliable sharing of sensitive personal health data.An optimized consensus mechanism streamlines the process into two stages,minimizing communication complexity among nodes and significantly improving the throughput of the blockchain system.Additionally,the introduction of advanced aggregate signature technology accommodates multi-user scenarios,reducing computational overhead for signature verification and enabling swift identification ofmalicious forgers.Comprehensive security analyses validate the system’s robustness and reliability.Simulation results demonstrate notable performance improvements over existing solutions,with reductions in computational overhead of up to 49.89%and communication overhead of up to 25.81%inmulti-user scenarios.Furthermore,the optimized consensus mechanism shows substantial efficiency gains across varying node configurations.This solution represents a significant step toward addressing the pressing challenges of health passport management in a secure,scalable,and efficient manner.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and...Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.展开更多
To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative t...To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.展开更多
Can the sharing economy contribute to urban sustainability?This study explores this question using the staggered entry of bike-sharing platforms across Chinese cities as a quasi-natural experiment.Drawing on 2015-2017...Can the sharing economy contribute to urban sustainability?This study explores this question using the staggered entry of bike-sharing platforms across Chinese cities as a quasi-natural experiment.Drawing on 2015-2017 city characteristics panel data and monthly CO_(2) emission records,we employ a staggered difference-in-differences(DID)model to assess the impact of bike-sharing on per capita urban CO_(2) emissions.The results show that bike-sharing platforms significantly cut emissions by replacing high-carbon transport and boosting public adoption of shared bikes.This effect is more potent in cities with less stringent environmental enforcement,more advanced digital economies,greater technological innovation,and where two platforms jointly entered.By elucidating the mechanisms underlying bike-sharing’s carbon-reduction potential,this study highlights its role in urban sustainability.It offers policy insights for leveraging shared mobility to reduce city carbon emissions.展开更多
tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years f...tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.展开更多
0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has...0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.展开更多
Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.Howev...Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.展开更多
With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-...With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.展开更多
Learning from the wisdom and insights of others remains one of the most effective paths to progress.This is the driving force behind our commitment to experience sharing.The March/April issue features three renowned i...Learning from the wisdom and insights of others remains one of the most effective paths to progress.This is the driving force behind our commitment to experience sharing.The March/April issue features three renowned international standardization experts who share their journeys,the strategic"hows and whys"of global participation,and the critical role of stakeholder engagement in shaping the future of the industry.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either re...Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.展开更多
With the advent of the big data era,modern statistics has enjoyed unprecedented development opportunities and also faced numerous new challenges.Traditional statistical computing methods are often limited by issues su...With the advent of the big data era,modern statistics has enjoyed unprecedented development opportunities and also faced numerous new challenges.Traditional statistical computing methods are often limited by issues such as computer memory capacity and distributed storage of data across different locations,and are unable to directly apply to large-scale data sets.Therefore,in the context of big data,designing efficient and theoretically guaranteed statistical learning and inference algorithms has become a key issue that the current field of statistics urgently needs to address.In this paper,the application status of statistical analysis methods in the big data environment was systematically reviewed,and its future development directions were analyzed to provide reference and support for the further development of theory and methods of the statistical analysis of big data.展开更多
文摘Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.
基金supported by the National Natural Science Foundation of China(Grant No.U24B20146)the National Key Research and Development Plan in China(Grant No.2020YFB1005500)Beijing Natural Science Foundation Project(No.M21034).
文摘With the rapid development of web3.0 applications,the volume of data sharing is increasing,the inefficiency of big data file sharing and the problem of data privacy leakage are becoming more and more prominent,and the existing data sharing schemes have been difficult to meet the growing demand for data sharing,this paper aims at exploring a secure,efficient and privacy-protecting data sharing scheme under web3.0 applications.Specifically,this paper adopts interplanetary file system(IPFS)technology to realize the storage of large data files to solve the problem of blockchain storage capacity limitation,and utilizes ciphertext policy attribute-based encryption(CP-ABE)and proxy re-encryption(PRE)technology to realize secure multi-party sharing and finegrained access control of data.This paper provides the detailed algorithm design and implementation of data sharing phases and processes,and analyzes the algorithms from the perspectives of security,privacy protection,and performance.
文摘The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.
基金supported by the Natural Science Foundation of Hebei Province of China(F2021201052).
文摘With the rapid development of medical data sharing,issues of privacy and ownership have become prominent,which have limited the scale of data sharing.To address the above challenges,we propose a blockchainbased data-sharing framework to ensure data security and encourage data owners to actively participate in sharing.We introduce a reliable attribute-based searchable encryption scheme that enables fine-grained access control of encrypted data and ensures secure and efficient data sharing.The revenue distribution model is constructed based on Shapley value to motivate participants.Additionally,by integrating the smart contract technology of blockchain,the search operation and incentive mechanism are automatically executed.Through revenue distribution analysis,the incentive effect and rationality of the proposed scheme are verified.Performance evaluation shows that,compared with traditional data-sharing models,our proposed framework not only meets data security requirements but also incentivizes more participants to actively participate in data sharing.
基金supported by Hainan Provincial Natural Science Foundation of China Nos.622RC617,624RC485Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(SKLNST-2023-1-07).
文摘The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,including enterprises,agents,and government departments.However,the urgent issue that requires immediate attention is how to achieve secure and efficient cross-border data sharing among these government departments and enterprises in complex trade processes.In addressing this need,this paper proposes a data exchange architecture employing Multi-Authority Attribute-Based Encryption(MA-ABE)in combination with blockchain technology.This scheme supports proxy decryption,attribute revocation,and policy update,while allowing each participating entity to manage their keys autonomously,ensuring system security and enhancing trust among participants.In order to enhance system decentralization,a mechanism has been designed in the architecture where multiple institutions interact with smart contracts and jointly participate in the generation of public parameters.Integration with the multi-party process execution engine Caterpillar has been shown to boost the transparency of cross-border information flow and cooperation between different organizations.The scheme ensures the auditability of data access control information and the visualization of on-chain data sharing.The MA-ABE scheme is statically secure under the q-Decisional Parallel Bilinear Diffie-Hellman Exponent(q-DPBDHE2)assumption in the random oracle model,and can resist ciphertext rollback attacks to achieve true backward and forward security.Theoretical analysis and experimental results demonstrate the appropriateness of the scheme for cross-border data collaboration between different institutions.
基金supported by the National Natural Science Foundation of China(Grant No.72374081)the Key Research and Development Project of the Department of Science and Technology of Jilin Province(Grant No.20240304164SF).
文摘Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data sharing mechanisms.Design/methodology/approach:By integrating the UTAUT model,trust theory and self-efficacy theory,introducing the concepts of data transparency and individual innovation,and combining internal and external motivators,we constructed a conceptual model of medical data users’sharing behavior in clinical research scenarios.We conducted empirical research by collecting 360 pieces of first-hand data from clinical researchers.Findings:Among the internal motivators,effort expectation had a higher impact on sharing intention than performance expectation,individual innovation and self-efficacy had a higher impact on sharing behavior than trust.Trust does not show a significant impact on sharing intention,but it has a significant positive influence on sharing behavior.Among the external motivators,community influence and data transparency both positively affect sharing intention.In addition,users with different working years,professional status,data level needs,and different sharing experiences showed significant differences in healthcare data sharing.Research limitations:Our sample of clinical researchers from China was used as empirical data.Further research is needed to examine the generality of the study findings.Practical implications:The findings enhance healthcare data stakeholders’understanding of healthcare data sharing in clinical research scenarios and provide theoretical and practical insights for relevant researchers.Originality/value:In this study,the UTAUT model,trust theory and self-efficacy theory were integrated and applied to clinical research scenarios for the first time,and the concepts of data transparency and individual innovation were introduced,and the CRS-USB conceptual model was constructed and validated to extend the UTAUT model.
文摘In the process of implementing data openness between banks and fin-tech companies,as the breadth and depth of cooperation between banks and enterprises continue to increase,there is a risk of“too much correlation to fail”and“too many links to fail”.There are problems with the implementation of financial data openness by regulatory agencies for banks and fin-tech enterprises,such as the ambiguity of regulatory responsibilities,the emphasis on financial regulatory goals,and the lag in regulatory methods.To address these issues,it is necessary to clarify the responsibilities of financial regulatory agencies,establish a collaborative mechanism for financial regulation,coordinate the types of risks in bank enterprise cooperation,achieve the technical implementation of financial regulatory measures and the design of regulatory systems,obtain regulatory data in real time,establish a hierarchical regulatory system for bank enterprise cooperation to improve the regulatory path,and ensure the rational and legal use of financial data in bank enterprise cooperation.
文摘The growing demand for international travel has highlighted the critical need for reliable tools to verify travelers’healthcare status and meet entry requirements.Personal health passports,while essential,face significant challenges related to data silos,privacy protection,and forgery risks in global sharing.To address these issues,this study proposes a blockchain-based solution designed for the secure storage,sharing,and verification of personal health passports.This innovative approach combines on-chain and off-chain storage,leveraging searchable encryption to enhance data security and optimize blockchain storage efficiency.By reducing the storage burden on the blockchain,the system ensures both the secure handling and reliable sharing of sensitive personal health data.An optimized consensus mechanism streamlines the process into two stages,minimizing communication complexity among nodes and significantly improving the throughput of the blockchain system.Additionally,the introduction of advanced aggregate signature technology accommodates multi-user scenarios,reducing computational overhead for signature verification and enabling swift identification ofmalicious forgers.Comprehensive security analyses validate the system’s robustness and reliability.Simulation results demonstrate notable performance improvements over existing solutions,with reductions in computational overhead of up to 49.89%and communication overhead of up to 25.81%inmulti-user scenarios.Furthermore,the optimized consensus mechanism shows substantial efficiency gains across varying node configurations.This solution represents a significant step toward addressing the pressing challenges of health passport management in a secure,scalable,and efficient manner.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
基金supported by the International Partnership program of the Chinese Academy of Sciences(170GJHZ2023074GC)National Natural Science Foundation of China(42425706 and 42488201)+1 种基金National Key Research and Development Program of China(2024YFF0807902)Beijing Natural Science Foundation(8242041),and China Postdoctoral Science Foundation(2025M770353).
文摘Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.
文摘To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.72372048)the“Young Top-Notch Talent”Cultivation Program of Hubei Province.
文摘Can the sharing economy contribute to urban sustainability?This study explores this question using the staggered entry of bike-sharing platforms across Chinese cities as a quasi-natural experiment.Drawing on 2015-2017 city characteristics panel data and monthly CO_(2) emission records,we employ a staggered difference-in-differences(DID)model to assess the impact of bike-sharing on per capita urban CO_(2) emissions.The results show that bike-sharing platforms significantly cut emissions by replacing high-carbon transport and boosting public adoption of shared bikes.This effect is more potent in cities with less stringent environmental enforcement,more advanced digital economies,greater technological innovation,and where two platforms jointly entered.By elucidating the mechanisms underlying bike-sharing’s carbon-reduction potential,this study highlights its role in urban sustainability.It offers policy insights for leveraging shared mobility to reduce city carbon emissions.
基金supported by the National Natural Science Foundation of China(91959106)the Foundation of the Shanghai Municipal Education Commission(24RGZNC02)+4 种基金Shanghai Key Laboratory of Intelligent Information Processing,Fudan University(IIPL-2025-RD3-02)Key University Science Research Project of Anhui Province(2023AH030108)Climbing Peak Training Program for Innovative Technology team of Yijishan Hospital,Wannan Medical College(PF201904)Peak Training Program for Scientific Research of Yijishan Hospital,Wannan Medical College(GF2019G15)the talent project of the First Affiliated Hospital of Wannan Medical College(Yijishan Hospital of Wannan Medical College)(YR202422).
文摘tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.
基金supported by National Key R&D Program of China(No.2021YFF0501301)the National Natural Science Foundation of China(No.42172231)。
文摘0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.
基金supported by the National Key R&D Program of China[Grant No.2023YFF0713600]the National Natural Science Foundation of China[Grant No.62275062]+3 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant No.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Provincethe Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.
文摘With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.
文摘Learning from the wisdom and insights of others remains one of the most effective paths to progress.This is the driving force behind our commitment to experience sharing.The March/April issue features three renowned international standardization experts who share their journeys,the strategic"hows and whys"of global participation,and the critical role of stakeholder engagement in shaping the future of the industry.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
基金supported in part by the Research Fund of Key Lab of Education Blockchain and Intelligent Technology,Ministry of Education(EBME25-F-08).
文摘Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.
文摘With the advent of the big data era,modern statistics has enjoyed unprecedented development opportunities and also faced numerous new challenges.Traditional statistical computing methods are often limited by issues such as computer memory capacity and distributed storage of data across different locations,and are unable to directly apply to large-scale data sets.Therefore,in the context of big data,designing efficient and theoretically guaranteed statistical learning and inference algorithms has become a key issue that the current field of statistics urgently needs to address.In this paper,the application status of statistical analysis methods in the big data environment was systematically reviewed,and its future development directions were analyzed to provide reference and support for the further development of theory and methods of the statistical analysis of big data.
