Analysis of Security in Big Data Related to Healthcare

Big data facilitates the processing and management of huge amounts of data. In health, the main information source is the electronic health record with others being the Internet and social media. Health-related data refers to storage in big data based on and shared via electronic means. Why are criminal organisations interested in this data? These organisations can blackmail people with information related to their health condition or sell the information to marketing companies, etc. This article analyses healthcare-related big data security and proposes different solutions. There are different techniques available to help preserve privacy such as data modification techniques, cryptographic methods and protocols for data sharing, query auditing methods and others that are analysed in this research work. Although there remains much to do in the field of big data security, research in this area is moving forward, both from a scientific and commercial point of view

Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Secure big data ecosystem architecture : challenges and solutions

Big data ecosystems are complex data-intensive, digital–physical systems. Data-intensive ecosystems offer a number of benefits; however, they present challenges as well. One major challenge is related to the privacy and security. A number of privacy and security models, techniques and algorithms have been proposed over a period of time. The limitation is that these solutions are primarily focused on an individual or on an isolated organizational context. There is a need to study and provide complete end-to-end solutions that ensure security and privacy throughout the data lifecycle across the ecosystem beyond the boundary of an individual system or organizational context. The results of current study provide a review of the existing privacy and security challenges and solutions using the systematic literature review (SLR) approach. Based on the SLR approach, 79 applicable articles were selected and analyzed. The information from these articles was extracted to compile a catalogue of security and privacy challenges in big data ecosystems and to highlight their interdependencies. The results were categorized from theoretical viewpoint using adaptive enterprise architecture and practical viewpoint using DAMA framework as guiding lens. The findings of this research will help to identify the research gaps and draw novel research directions in the context of privacy and security in big data-intensive ecosystems. © 2021, The Author(s)

Introduction to Big Data Management Based on Agent Oriented Cyber Security, Journal of Telecommunications and Information Technology, 2017, nr 1

This paper deals with information security and safety issues in public open spaces. Public open spaces include high streets, street markets, shopping centers, community gardens, parks, and playgrounds, each of which plays a vital role in the social, cultural and economic life of a community. Those outdoor public places are mashed up with various ICT tools, such as video surveillance, smartphone apps, Internet of Things (IoT) technologies, and biometric big data (called Cyber Parks). Security and safety in public places may include video surveillance of movement and the securing of personalized information and location-based services. The article introduces technologies used in Cyber Parks to achieve information security in big data era

Distributed Machine Learning Architecture for Security Improvement in Computer Drafting and Writing in Art Asset Identification System

Art asset identification service is becoming increasingly important in the art market, where the value of art assets is constantly changing. The service provides authentication, evaluation, and provenance research for artworks, which helps art collectors and institutions to protect their investments and ensure the authenticity of their collections. The effective management of big data is critical for the art asset identification service, and there are several big data management technologies that can be achieved. To improve security in the big data Management model uses Distributed Associative Rule Mining is implemented with Hashing based Symmetric Key Cryptography. The designed model comprises of Associate Rule Hashing Symmetric Key (ARHSK). The proposed ARHSK model comprises the symmetric key generated with the hashing model to secure art assets. With the ARHSK information is stored and processed for security features. The performance of the ARHSK model is implemented with the machine learning model for classification. Simulation analysis expressed that ARHSK exhibits an improved classification accuracy of 99.67% which is ~13% higher than the CNN and ANN models