Design of Personnel Big Data Management System Based on Blockchain

With the continuous development of information technology, enterprises, universities and governments are constantly stepping up the construction of electronic personnel information management system. The information of hundreds of thousands or even millions of people’s information are collected and stored into the system. So much information provides the cornerstone for the development of big data, if such data is tampered with or leaked, it will cause irreparable serious damage. However, in recent years, electronic archives have exposed a series of problems such as information leakage, information tampering, and information loss, which has made the reform of personnel information management more and more urgent. The unique characteristics of the blockchain, such as non-tampering and traceability make it have great application potential in personnel information management, and can effectively solve many problems of traditional file management. However, the blockchain is limited by its own shortcomings such as small storage space and slow synchronization time, and cannot be directly applied to the big data field. This paper proposes a personnel management system based on blockchain, we analyzed the defects of the blockchain and proposed an improved method, constructs a novel data storage model of on-chain and out-of-chain that can effectively solve the problem of data redundancy and insufficient storage space. Based on this, we developed a prototype system with query, add, modify, and track personnel information, verified the feasibility of applying blockchain to personnel information management, explore the possibility of combining blockchain with big data

MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning

Mobile phones and other electronic gadgets or devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data. Mobile health data use mobile devices to gather clinical health data and track patient vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on mhealth. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in specificity, and 99.66% in F1 score. Our research indicated a promising future in mhealth being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.Comment: 6, Accepted by 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE

A Novel Approach for Mining Big Data Using Multi-Model Fusion Mechanism (MMFM)

Big data processing and analytics require sophisticated systems and cutting-edge methodologies to extract useful data from the available data. Extracted data visualization is challenging because of the processing models' dependence on semantics and classification. To categorize and improve information-based semantics that have accumulated over time, this paper introduces the Multi-model fusion mechanism for data mining (MMFM) approach. Information dependencies are organized based on the links between the data model based on attribute values. This method divides the attributes under consideration based on processing time to handle complicated data in controlled amount of time. The proposed MMFM’s performance is assessed with real-time weather prediction dataset where the data is acquired from sensor (observed) and image data. MMFM is used to conduct semantic analytics and similarity-based classification on this collection. The processing time based on records and samples are investigated for the various data sizes, instances, and entries. It is found that the proposed MMFM gets 70 seconds of processing time for 2GB data and 0.99 seconds while handling 5000 records for various classification instances

Authentication Based on Blockchain

Across past decade online services have enabled individuals and organizations to perform different types of transactions such as banking, government transactions etc. The online services have also enabled more developments of applications, at cheap cost with elastic and scalable, fault tolerant system. These online services are offered by services providers which are use authentication, authorization and accounting framework based on client-server model. Though this model has been used over decades, study shows it is vulnerable to different hacks and it is also inconvenient to use for the end users. In addition, the services provider has total control over user data which they can monitor, trace, leak and even modify at their will. Thus, the user data ownership, digital identity and use of online services has raised privacy and security concern for the users. In this thesis, Blockchain and the e-pass application are studied and alternative model for authentication, authorization and accounting is proposed based on Ethereum Blockchain. Furthermore, a prototype is developed which enables users to consume online services by authenticating, authorizing, and accounting with a single identity without sharing any private user data with the services provider center server. Experiments are run with the prototype to verify that it works as expected. Measurements are done to assess the feasibility and scalability of the solution. In the final part of the thesis, pros and cons of the proposed solution are discussed and perspectives for further research are sketched

Blockchain Technology for Secure Accounting Management: Research Trends Analysis

The scope of blockchain technology, initially associated with the cryptocurrency Bitcoin, is greater due to the multiple applications in various disciplines. Its use in accounting lies mainly in the fact that it reduces risks and the eventuality of fraud, eliminates human error, promotes efficiency, and increases transparency and reliability. This means that different economic sectors assume it as a recording and management instrument. The aim is to examine current and emerging research lines at a global level on blockchain technology for secure accounting management. The evolution of the publication of the number of articles between 2016 and 2020 was analyzed. Statistical and mathematical techniques were applied to a sample of 1130 records from the Scopus database. The data uncovered a polynomial trend in this period. The seven main lines of work were identified: blockchain, network security, information management, digital storage, edge computing, commerce, and the Internet of Things. The ten most outstanding emerging research lines are detected. This study provides the past and future thematic axes on this incipient field of knowledge, which is a tool for decision-making by academics, researchers, and directors of research investment program

Blockchain Technology for Higher Education and Recruitment: A Systematic Literature Review

Blockchain is a recent technological innovation that has undergone significant growth with numerous business applications. One of the most promising applications of blockchain technology lies within the domains of higher education and recruitment. Despite its potential, academic literature on these topics remains limited. In this study, the researchers conducted a systematic literature review to unravel blockchain’s potential applications in higher education and recruitment. This paper identifies the current benefits and challenges of blockchain in these fields and delineates key steps in higher education and recruitment processes to determine blockchain practical applications. To analyze the potential benefits and challenges of blockchain for higher education and recruitment, we integrate the resource-based view (RBV) and transaction cost theory (TCT). The RBV is employed to underscore blockchain’s potential as a strategic resource that can provide competitive advantages in higher education, while the TCT is utilized to focus on its efficacy in reducing transaction costs related to recruitment. The study concludes by highlighting directions for future research, emphasizing the imperative for empirical investigations into real-world blockchain applications. It also encourages theoretical advancements to deepen our understanding of the impact of blockchain technology on higher education and recruitment

Blockchain-Based Authentication and Trust Management Mechanism for Smart Cities

Security has always been the main concern for the internet of things (IoT)-based systems. Blockchain, with its decentralized and distributed design, prevents the risks of the existing centralized methodologies. Conventional security and privacy architectures are inapplicable in the spectrum of IoT due to its resource constraints. To overcome this problem, this paper presents a Blockchain-based security mechanism that enables secure authorized access to smart city resources. The presented mechanism comprises the ACE (Authentication and Authorization for Constrained Environments) framework-based authorization Blockchain and the OSCAR (Object Security Architecture for the Internet of Things) object security model. The Blockchain lays out a flexible and trustless authorization mechanism, while OSCAR makes use of a public ledger to structure multicast groups for authorized clients. Moreover, a meteor-based application is developed to provide a user-friendly interface for heterogeneous technologies belonging to the smart city. The users would be able to interact with and control their smart city resources such as traffic lights, smart electric meters, surveillance cameras, etc., through this application. To evaluate the performance and feasibility of the proposed mechanism, the authorization Blockchain is implemented on top of the Ethereum network. The authentication mechanism is developed in the node.js server and a smart city is simulated with the help of Raspberry Pi B+. Furthermore, mocha and chai frameworks are used to assess the performance of the system. Experimental results reveal that the authentication response time is less than 100 ms even if the average hand-shaking time increases with the number of clients

Integrating Wearable Technology for Enhanced Self-Assessment in Mental Health

Mental health is a critical aspect of overall well-being and affects a large proportion of the world’s population. The first step of identifying possible issues in an individual is often using assessment method such as surveys, forms, or questionnaires. However, traditional assessment methods such as self-assessment questionnaires yield challenges and limitations like social desirability and response bias. These traditional assessment methods rely heavily on the patient’s recollection of events, feelings, and current psychological state to work as intended. The emergence of ubiquitous wearable technology shows promise that it can help mitigate the mentioned issue. These devices promise to collect data reliably and can be used to get an objective representation of the physiological state of a patient. In addition, it might lead to better response rates and a more enhanced patient experience. However, thorough testing and evaluation is needed when integrating these emerging technologies. This thesis research the pressing need to improve these traditional assessment methods used in mental health by leveraging the potential of wearable technology. Moreover, the aim is to demonstrate how wearable technology can be integrated into self-assessment questionnaires through the development of an artifact that promotes reuse and interoperability. It consists of three general components: the questionnaire, the corresponding response, and the wearable data collection process for specific domains through digital biomarkers. The evaluation process involved a semi-structured interview, object-based evaluation experiment, and a user acceptance survey of the artifact. Based on this, our artifact poses as a viable solution and can be used as a starting point for future research in the problem domain.Masteroppgave i Programvareutvikling samarbeid med HVLPROG399MAMN-PRO

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security