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

SAS-HRM: Secure Authentication System for Human Resource Management

To guarantee data confidentiality and information sensitivity, human resource management requires secure systems. In the field of authorization and dependability in recognizing and identifying persons, facial recognition has grown in importance. In this research, a secure authentication system is proposed based on biometric aspects of the user's face and identifying it using the CNN classification model is provided to give access to human resource management and update data. The system is divided into four major stages: First, set up the system environment, beginning with smart cards, card readers, Arduino, and so on. Second, after undergoing pre-treatment steps, the facial characteristics are extracted using LDA. Third, create a high-accuracy CNN model to recognize and classify the user's face among the system's users. Finally, the user is allowed to enter the system and update his information. When compared to the accuracy of classification using machine learning techniques with a CNN proposed model, the accuracy of the model with LDA was up to 100%. K-NN has 91%, while TD has 94%

Big data analytics: balancing individuals’ privacy rights andbusiness interests

This research thesis analyses and discusses the importance of having a legal framework that can control and manage the use of data during the Big Data analysis process. The thesis firstly examines the data analytics technologies, such as Hadoop Distributed File System (HDFS) and the technologies that are used to protect data during the analytics process. Then there is an examination of the legal principles that are part of the new General Data Protection Regulation (GDPR), and the other laws that are in place in order to manage the new era of Big Data analytics. Both the legal principles Chapter and data analytics Chapter are part of the literature review. The IT section of the literature review begins with an analysis of the data analytics technologies, such as HDFS and Map-Reduce. The second part consists of the technologies to protect privacy, especially with respect to protection during the data generation phase. Furthermore, there is a discussion on whether these current technologies are good enough to provide protection for personal data in the Big Data age. The legal section of the literature review starts by discussing some risk mitigation schemes that can be used to help individuals protect their data. This is followed by an analysis of consent issues in the Big Data era and later by an examination of the important legal principles that can help to control the Big Data process and ultimately protect individuals’ personal data. The motivation for carrying out this research was to examine how Big Data could have an effect on ordinary individuals, specifically with respect to how their data and privacy could be infringed during the data analytics process. This was done by bringing together the Big Data worlds from the legal and technological perspective. Also, by hearing the thoughts and views of those individuals who could be affected, and hearing from the experts who could shine a light on the realities in the Big Data era. The research includes the analysis and results of three surveys, constituting over 100 respondents, who expressed their views on a number of issues, including their fears about privacy online. This included a survey of mainly closed questions for students at Canterbury Christ Church University, a survey monkey survey for students at University College Cork, in Ireland and finally a survey for students in Sri Lanka. Questions were posed to some experts in areas of IT law and Big Data analytics and security. The results of these interviews were analysed and discussed, producing much debate with respect to what can be done to manage and protect citizens’ personal data privacy in the age of Big Data analytics. The software packages Statistical Package for the Social Sciences (SPSS) and Minitab were used to analyse the results of the surveys, while Qualitative Data Analysis Miner (QDA miner) software was used to analyse the results of the interviews

Strategies for Reducing the Risk of Data Breach Within the Internet Cloud

Businesses are increasingly incorporating cloud computing into their current business models. With this increase, security breach exposure has also increased, causing business leaders to be concerned with financial hardship, operational disruption, customer turnover, and customer confidence loss due to personal data exposure. Grounded in the integrated system theory of information security management, the purpose of this qualitative multiple case study was to explore successful strategies some information security leaders in the aerospace and defense contractor industry use to protect cloud-based data from security breaches. The participants were 7 information security leaders from 7 different aerospace and defense contractor companies located in the United States mid-Atlantic region. Data from semistructured interviews were analyzed and compared with 8 publicly available data sources for data triangulation. Emergent themes narrowing this knowledge gap was extracted through an analysis technique such as coding and then triangulated. The recurring themes were (a) strong authentication methods, (b) encryption, and (c) personnel training and awareness. A key recommendation includes information security leaders implementing preventative security measures while improving an organization\u27s ability to protect data lost within the Internet cloud. The implications for positive social change include the potential to increase consumers confidence while protecting confidential consumer data and organizational resources, protecting customers from the costs, lost time, and recovery efforts associated with identity theft