A Fair and Trusted Trading Scheme for Medical Data Based on Smart Contracts

Data is regarded as a valuable asset,and sharing data is a prerequisite for fully exploiting the value of data.However,the current medical data sharing scheme lacks a fair incentive mechanism,and the authenticity of data cannot be guaranteed,resulting in low enthusiasm of participants.A fair and trusted medical data trading scheme based on smart contracts is proposed,which aims to encourage participants to be honest and improve their enthusiasm for participation.The scheme uses zero-knowledge range proof for trusted verification,verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction,and realizes privacy protection.At the same time,the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction price.The smart contract is used to complete the verification and game bargaining process,and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction denial.Finally,by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations,it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized environment.The experimental results show that the model improves the credibility and fairness of medical data transactions,maximizes social benefits,encourages more patients and medical institutions to participate in the circulation of medical data,and more fully taps the potential value of medical data.

Data Flow&Transaction Mode Classification and An Explorative Estimation on Data Storage&Transaction Volume

The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete picture of data flaw and transaction,this paper presents a systematic overview of the flow and transaction of personal,corporate and public data on the basis of data factor classification from various perspectives.By utilizing various sources of information,this paper estimates the volume of data generation&storage and the volume&trend of data market transactions for major economies in the world with the following findings:(i)Data classification is diverse due to a broad variety of applying scenarios,and data transaction and profit distribution are complex due to heterogenous entities,ownerships,information density and other attributes of different data types.(ii)Global data transaction has presented with the characteristics of productization,servitization and platform-based mode.(iii)For major economies,there is a commonly observed disequilibrium between data generation scale and storage scale,which is particularly striking for China.(i^v)The global data market is in a nascent stage of rapid development with a transaction volume of about 100 billion US dollars,and China s data market is even more underdeveloped and only accounts for some 10%of the world total.All sectors of the society should be flly aware of the diversity and complexity of data factor classification and data transactions,as well as the arduous and long-term nature of developing and improving relevant institutional systems.Adapting to such features,efforts should be made to improve data classification,enhance computing infrastructure development,foster professional data transaction and development institutions,and perfect the data governance system.

Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network:A Comprehensive Survey

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance,transportation,healthcare,education,and supply chain management.Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges.However,the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes.There is the biggest challenge of data integrity and scalability,including significant computing complexity and inapplicable latency on regional network diversity,operating system diversity,bandwidth diversity,node diversity,etc.,for decision-making of data transactions across blockchain-based heterogeneous networks.Data security and privacy have also become the main concerns across the heterogeneous network to build smart IoT ecosystems.To address these issues,today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with blockchain.The key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous network.This paper proposes a full-fledged taxonomy to identify the main obstacles,research gaps,future research directions,effective solutions,andmost relevant blockchain-enabled cybersecurity systems.In addition,Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper tomeet the goal of maintaining optimal performance data transactions among organizations.Overall,this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it presents a potential cybersecurity design with key requirements of blockchain across a heterogeneous network.

Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network

Credit risk prediction for small and medium enterprises(SMEs)has long posed a complex research challenge.Traditional approaches have primarily focused on enterprise-specific variables,but these models often prove inadequate when applied to SMEs with incomplete data.In this innovative study,we push the theoretical boundaries by leveraging data from adjacent enterprises to address the issue of data deficiency.Our strategy involves constructing an intricate network that interconnects enterprises based on shared managerial teams and business interactions.Within this network,we propose a novel relational graph attention network(RGAT)algorithm capable of capturing the inherent complexity in its topological information.By doing so,our model enhances financial service providers'ability to predict credit risk even in the face of incomplete data from target SMEs.Empirical experiments conducted using China's SMEs highlight the predictive proficiency and potential economic benefits of our proposed model.Our approach offers a comprehensive and nuanced perspective on credit risk while demonstrating the advantages of incorporating network-wide data in credit risk prediction.

DBT-PDP:Provable data possession with outsourced data batch transfer based on blockchain

In the scenario of large-scale data ownership transactions,existing data integrity auditing schemes are faced with security risks from malicious third-party auditors and are inefficient in both calculation and communication,which greatly affects their practicability.This paper proposes a data integrity audit scheme based on blockchain where data ownership can be traded in batches.A data tag structure which supports data ownership batch transaction is adopted in our scheme.The update process of data tag does not involve the unique information of each data,so that any user can complete ownership transactions of multiple data in a single transaction through a single transaction auxiliary information.At the same time,smart contract is introduced into our scheme to perform data integrity audit belongs to third-party auditors,therefore our scheme can free from potential security risks of malicious third-party auditors.Safety analysis shows that our scheme is proved to be safe under the stochastic prediction model and k-CEIDH hypothesis.Compared with similar schemes,the experiment shows that communication overhead and computing time of data ownership transaction in our scheme is lower.Meanwhile,the communication overhead and computing time of our scheme is similar to that of similar schemes in data integrity audit.

Bank transaction data modeling by optimized hybrid machine learning merged with ARIMA

The bank transactions are needed to be modeled to predict the future transactions of the banks based on the previous transactions.In order to achieve efficient modeling of bank data transactions,Deep Belief Network(DBN)and Neural network(NN)classifiers are used in this paper.Initially,the bank transaction data such as transaction count and amount are subjected to feature extraction to extract the statistical features.Now,the extracted data are modeled using the combination of DBN and NN models,where the average modeled output from both the network is considered as the final result.The above procedure is utilized for the two prediction models such as transaction count and transaction amount.Moreover,the transaction count from prediction model 1 is subjected to the Auto-Regressive Integrated Moving Average(ARIMA)model to compute the relationship between the transition count and transition amount.Here,as the main contribution,the number of hidden neurons in both DBN and NN are optimized or tuned accurately using the hybridized optimization models with Lion Algorithm(LA),and Artificial Bee Colony(ABC)named L-ABC model.The average of entire transactional amounts,i.e.the modeled outputs are matched with the actual data to validate the performance of the implemented model.

同位模式挖掘中一种改进的极大团事务化算法

发现不同空间对象类型的同位关系是重要的空间数据挖掘问题.研究了目前提出的2类典型同位模式挖掘算法,提出了一种改进的极大团空间事务化算法(CoreClique),该算法以核心团为基础来产生极大团,避免了核心团内部实例点成团的计算量,通过核心团与扩展团的结合可较全面地发现空间中的极大团信息.实验表明,该算法可以有效地产生极大团,对空间数据进行事务化处理.

Effect of Count Estimation in Finding Frequent Itemsets over Online Transactional Data Streams

A data stream is a massive unbounded sequence of data elements continuouslygenerated at a rapid rate. Due to this reason, most algorithms for data streams sacrifice thecorrectness of their results for fast processing time. The processing time is greatly influenced bythe amount of information that should be maintained. This issue becomes more serious in findingfrequent itemsets or frequency counting over an online transactional data stream since there can bea large number of itemsets to be monitored. We have proposed a method called the estDec method forfinding frequent itemsets over an online data stream. In order to reduce the number of monitoreditemsets in this method, monitoring the count of an itemset is delayed until its support is largeenough to become a frequent itemset in the near future. For this purpose, the count of an itemsetshould be estimated. Consequently, how to estimate the count of an itemset is a critical issue inminimizing memory usage as well as processing time. In this paper, the effects of various countestimation methods for finding frequent itemsets are analyzed in terms of mining accuracy, memoryusage and processing time.

A Theory of Digital Economy Development:An Examination of the Practice of Chinese Platforms

Grasping the development trends and patterns of the digital economy and promoting the integrated development of the digital and the real economy is a strategic choice for China's construction of new competitive advantages.Based on case studies of the growth of e-commerce,this paper develops five microeconomic propositions about the following issues:the microeconomic features of data factor that differ from traditional production factors;optimal decision-making for digital enterprises;“data+platform”architecture,data transaction governance,and digital infrastructure supply.We apply these theoretical propositions to enterprise innovative practices in different application scenarios such as driverless vehicles and manufacturing digitalization.This paper provides a systematic and consistent theoretical framework for analyzing platform business model innovation and accurately identifying issues of institutional construction that promote the integrated development of the digital and the real economy.

Design and Implementation of Spatial Data Watermarking Service System

Nowadays, more and more digitalized spatial data are sold and transmitted on the Internet. Thus, there arises an important issue about copyright protection of the digital data. To solve this problem, this paper has designed and implemented a spatial data watermarking service (SDWS) system which can provide a secure framework for data transaction and transfer via the Internet and protect the rights of both copyright owners and consumers at the same time.

Research on the Realization Path and Application of a Data Governance System Based on Data Architecture

The construction and development of the digital economy,digital society and digital government are facing some common basic problems.Among them,the construction of the data governance system and the improvement of data governance capacity are short boards and weak links,which have seriously restricted the construction and development of the digital economy,digital society and digital government.At present,the broad concept of data governance goes beyond the scope of traditional data governance,which“involves at least four aspects:the establishment of data asset status,management system and mechanism,sharing and openness,security and privacy protection”.Traditional information technologies and methods are powerless to comprehensively solve these problems,so it is urgent to improve understanding and find another way to reconstruct the information technology architecture to provide a scientific and reasonable technical system for effectively solving the problems of data governance.This paper redefined the information technology architecture and proposed the data architecture as the connection link and application support system between the traditional hardware architecture and software architecture.The data registration system is the core composition of the data architecture,and the public key encryption and authentication system is the key component of the data architecture.This data governance system based on the data architecture supports complex,comprehensive,collaborative and cross-domain business application scenarios.It provides scientific and feasible basic support for the construction and development of the digital economy,digital society and digital government.

Exploratory analysis of grocery product networks

Finding meaningful sets of co-purchased products allows retailers to manageinventory better and develop market strategies. Analyzing the baskets ofproducts, known as market basket analysis, is typically carried out usingassociation rule mining or community detection approach. This article usesboth methods to investigate a transaction dataset collected from a brick-andmortargrocery store. The findings reveal interesting purchasing patterns oflocal residents and prompt us to consider dynamic modeling of the productnetwork in the future.